# Part 1 of 4 -- r3-b1 (NAMAMI/India), items 1-12 of 24

Part of the Popper Engine adversarial-rivals Grok pack. See `GROK_PACK_20260806.md` for full operator instructions. Paste each numbered block below as ONE Grok chat turn (a fresh block = a fresh, unrelated turn -- do not chain them in the same conversation, so one item's evidence never leaks into another's answer). Save each reply as raw text into the drop folder named at the end of that block.


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CONJECTURE r3-b1-01  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** NAMAMI's dated-fraction (share of records with a resolved date, date_precision != 'unknown') stays under 1% in every complete state, and is at least an order of magnitude below every other landed manuscript-catalogue source that exposes date fields at all.

**Proposed mechanism:** This is a harvest-method artifact, not a claim about actual production chronology: NAMAMI's list-page enumeration schema does not parse a date column at all, unlike sources built from richer per-manuscript records.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** All 11 complete states show date_precision='unknown' on exactly 100% of records (0 dated records, 0 exceptions, across ~4.22M rows). Comparator sources' dated fractions: Ktiv 25.14%, SfarData 99.41%, Digital Scriptorium 56.87%, MPO 75.23%, NGMCP 24.88% (the lowest comparator), Fihrist 67.05%, HMML 90.29%, Korea/korcis 58.38% -- i.e. NAMAMI's training-side rate sits more than 2,000x below the lowest comparator.

**Holdout test and outcome:** Tested (with a filter excluding rows touched by a separate detail-enrichment pilot, so that unrelated pilot cannot leak date data into this count) across all nine India holdout states/UTs. No holdout state showed a dated-fraction rising above the training pattern.

**Status:** Survived every tested holdout state.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-01
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-01.md
################################################################

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CONJECTURE r3-b1-03  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** Independently-curated European binding-waste fragment collections (built from reused/recycled parchment, not deposit-and-preserve archives) converge on a mean cataloged date inside [1150, 1450] -- distinct from, and later than, Cairo Genizah fragment material, even though both are the same physical unit (fragment).

**Proposed mechanism:** Binding waste and a preservation-focused fragment survey are proposed to be distinct chronological populations from a Genizah-style storage/deposit trove, reflecting different real-world processes that generate loose fragments.

**Scope:** This conjecture concerns the manuscript-catalogue source(s) named below only -- it does not involve NAMAMI or India data at all, and is not decided by NAMAMI's India-state holdout in any way (no India state's landing can confirm or refute it). It is decided only by future growth of, or new landings to, the specific source(s) named below; no such test has run yet. Specifically: two independently-curated binding-waste fragment sources (a Swedish archival collection and a general European fragment survey), and a Genizah-fragment comparison subset of a third source, all restricted to the 'fragment' unit only (never summed with whole-manuscript counts).

**Training-side evidence:** The two binding-waste sources converge within 0.3 years of each other: mean cataloged date 1280.61 (n=17,234 dated of 22,909) and 1280.29 (n=6,946 dated of 7,126). The Genizah-fragment comparison subset has mean 1471.71 (n=4,272 dated of 43,017, a much lower 9.9% dated rate) -- about 190 years later on average than the binding-waste pair, which already revises a naive 'fragments are the oldest material' prior.

**Holdout test and outcome:** No holdout test has been run for this conjecture. Evaluate the rival mechanisms against the training evidence alone.

**Status:** Not yet tested; decided only by future data.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-03
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-03.md
################################################################

################################################################
CONJECTURE r3-b1-04  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** The medium_class mix of Korea's national manuscript-catalogue source (the split between manuscript, woodblock, movable_type, and unclassified rows) is stable within +/-10 percentage points per category as the source grows.

**Proposed mechanism:** A stability/no-regime-change claim: organic growth of the source is expected to leave the internal proportions of its medium-classification roughly fixed, absent a change in the classification pipeline itself or a sharply different period of material being added.

**Scope:** This conjecture concerns the manuscript-catalogue source(s) named below only -- it does not involve NAMAMI or India data at all, and is not decided by NAMAMI's India-state holdout in any way (no India state's landing can confirm or refute it). It is decided only by future growth of, or new landings to, the specific source(s) named below; no such test has run yet. Specifically: Korea's national manuscript-catalogue source (499,003 rows at registration).

**Training-side evidence:** Baseline shares (this measurement IS the registered baseline, confirmed essentially unchanged from an earlier snapshot days before): unknown 64.56% (322,148), manuscript 16.05% (80,083), woodblock 12.65% (63,128), movable_type 6.74% (33,644).

**Holdout test and outcome:** No holdout test has been run for this conjecture. Evaluate the rival mechanisms against the training evidence alone.

**Status:** Not yet tested; decided only by future growth of this one source.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-04
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-04.md
################################################################

################################################################
CONJECTURE r3-b1-05  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** The average count of individual-text-within-a-composite-manuscript rows per parent manuscript record is source-specific and reflects manuscript culture, not a universal constant: a Byzantine/Greek-codex catalogue shows markedly higher text-per-manuscript density than a Perso-Arabic catalogue or an English lost-book catalogue, while a fourth (title-grain) source shows exactly 1:1 by construction.

**Proposed mechanism:** Composite-codex density is proposed to reflect real codicological/manuscript-culture differences -- how many separate texts get bound into one physical volume in a given tradition -- not a cataloguing-depth artifact of one project.

**Scope:** This conjecture concerns the manuscript-catalogue source(s) named below only -- it does not involve NAMAMI or India data at all, and is not decided by NAMAMI's India-state holdout in any way (no India state's landing can confirm or refute it). It is decided only by future growth of, or new landings to, the specific source(s) named below; no such test has run yet. Specifically: four sources that populate a contained-text table at all (a Byzantine/Greek-manuscript catalogue, a Perso-Arabic catalogue, an English lost-book catalogue, and a Chinese Buddhist canon catalogue).

**Training-side evidence:** Ratios (contained-text rows per parent record): 6.53 (260,182/39,842), 2.43 (37,176/15,318), 1.01 (8,300/8,199), and exactly 1.00 (5,005/5,005 -- flagged as very likely a structural artifact of how that fourth source's harvester constructs its rows, not an emergent codicological fact).

**Holdout test and outcome:** No holdout test has been run for this conjecture. Evaluate the rival mechanisms against the training evidence alone.

**Status:** Not yet tested; decided only by future landings/increments to these sources.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-05
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-05.md
################################################################

################################################################
CONJECTURE r3-b1-06  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** A large Iranian union catalogue has a materially lower institutional-attribution completeness rate (share of records with a resolved holding institution) than every other checked large source, and this gap is stable as the catalogue grows.

**Proposed mechanism:** A catalogue-construction/harvest-completeness fact about this one union catalogue specifically (which aggregates contributions from many institutions with uneven metadata practice), not a claim about Iranian manuscript production or survival.

**Scope:** This conjecture concerns the manuscript-catalogue source(s) named below only -- it does not involve NAMAMI or India data at all, and is not decided by NAMAMI's India-state holdout in any way (no India state's landing can confirm or refute it). It is decided only by future growth of, or new landings to, the specific source(s) named below; no such test has run yet. Specifically: one large Iranian union catalogue (323,111 rows), compared against seven other already-landed large sources.

**Training-side evidence:** The union catalogue's institution-attribution completeness is 69.86% (225,714/323,111). Every comparator is much higher: NAMAMI, Ktiv, Digital Scriptorium, HMML, and Fihrist all 100%, Korea/korcis 99.89%, a cuneiform-tablet catalogue 94.68% (the next-lowest).

**Holdout test and outcome:** No holdout test has been run for this conjecture. Evaluate the rival mechanisms against the training evidence alone.

**Status:** Not yet tested; decided only by future increments to this one source.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-06
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-06.md
################################################################

################################################################
CONJECTURE r3-b1-07  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** Within one manuscript catalogue's own scribe-attestation layer, the share of manuscripts with two or more recorded scribe-attestation events is a stable structural baseline (registered band [3%, 9%]) as the corpus grows.

**Proposed mechanism:** Proposed to reflect a real, stable base rate of multi-scribe or collaborative production in the underlying corpus. A sharp future jump in this rate would more likely indicate a change in the cataloguing project's own recording practice than a shift in historical production patterns -- flagged by the claim itself as the confound to watch for.

**Scope:** This conjecture concerns the manuscript-catalogue source(s) named below only -- it does not involve NAMAMI or India data at all, and is not decided by NAMAMI's India-state holdout in any way (no India state's landing can confirm or refute it). It is decided only by future growth of, or new landings to, the specific source(s) named below; no such test has run yet. Specifically: one source with a dedicated scribe-attestation layer (5,912 manuscript records, 6,375 scribe-attestation event rows; attestations count events per record, not deduplicated scribe identities across the corpus).

**Training-side evidence:** Attestation-count distribution: ordinal 1 (every attested record) 5,912; ordinal 2, 318 records (5.38% of attested records have a second scribe); ordinal 3, 71; decaying to a single record at ordinal 11.

**Holdout test and outcome:** No holdout test has been run for this conjecture. Evaluate the rival mechanisms against the training evidence alone.

**Status:** Not yet tested; decided only by this one source's own future growth.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-07
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-07.md
################################################################

################################################################
CONJECTURE r3-b1-08  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** NAMAMI's folio/leaf-count field is populated on well under 1% of records in every complete state, in sharp contrast to the closest thematic comparator (an independently-curated Sanskrit/Himalayan-manuscript catalogue), which populates the equivalent field on the large majority of its records.

**Proposed mechanism:** A harvest-method-completeness fact: NAMAMI's list-page enumeration method does not parse an extent/folio-count field, versus the comparator's richer per-manuscript cataloguing. Explicitly not a claim about the physical extent of the underlying manuscripts.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it. Comparator: an independently-curated Sanskrit/Himalayan-manuscript catalogue (15,812 rows).

**Training-side evidence:** All 11 complete states show extent populated on EXACTLY 0% of records. The comparator populates it on 97.63% of records (15,438/15,812), mean 52.67 leaves, range 1-1,757. Separately: the small fraction of NAMAMI records nationwide that DO carry this field (about 0.27% of all records at the time) is 100% concentrated outside these 11 states -- flagged as a live risk that a holdout state's landing could hit a different (richer) data-entry pathway than the 11 training states did.

**Holdout test and outcome:** Tested (with a filter excluding rows touched by a separate detail-enrichment pilot known to populate this exact field, so that unrelated pilot's rows cannot leak into a holdout state's count) across all nine India holdout states/UTs. Stayed under the training-pattern ceiling throughout.

**Status:** Survived every tested holdout state.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-08
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-08.md
################################################################

################################################################
CONJECTURE r3-b1-09  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** The rate of exact-precision dating (as opposed to a date range, or no date at all) is source-specific and spans a wide but stable range across already-landed catalogue sources; no single source's rate is a universal constant, but each source's OWN rate is stable (within a +/-15 percentage-point band) as that source grows.

**Proposed mechanism:** Exact-dating rate is proposed to reflect each source's own cataloguing tradition and instrument, not source size -- a clean 'bigger corpus = less precise dating' relationship was explicitly checked for and rejected (the two largest sources checked are both large AND high-exact-rate).

**Scope:** This conjecture concerns the manuscript-catalogue source(s) named below only -- it does not involve NAMAMI or India data at all, and is not decided by NAMAMI's India-state holdout in any way (no India state's landing can confirm or refute it). It is decided only by future growth of, or new landings to, the specific source(s) named below; no such test has run yet. Specifically: eight already-landed non-India sources (a Hebrew-manuscript catalogue, a Swedish archival fragment collection, a Sephardi-manuscript database, a Perso-Arabic catalogue, an Islamic-manuscript union catalogue, a Christian-Oriental catalogue, a large Western-manuscript aggregator, and Korea's national catalogue).

**Training-side evidence:** Exact-precision rates: 10.76%, 2.46%, 72.53% (a dated-manuscript-selected corpus by construction, flagged as a scope caveat), 23.17%, 25.98%, 11.42%, 44.99%, 56.47%.

**Holdout test and outcome:** No holdout test has been run for this conjecture. Evaluate the rival mechanisms against the training evidence alone.

**Status:** Not yet tested; decided only by future increments to these eight sources.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-09
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-09.md
################################################################

################################################################
CONJECTURE r3-b1-16  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** The distinct-institution density (distinct holding-institution names per 1,000 records) does not exceed 103 per 1,000 in any complete state.

**Proposed mechanism:** Proposed to be a legacy-of-local-collecting-culture effect specific to each state, not a scale effect -- a clean power-law relationship between record count and institution count was explicitly checked for and rejected (the largest state by record count has one of the LOWEST densities; the second-largest has the HIGHEST).

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** Density per 1,000 records ranges from West Bengal 1.10 (the lowest, a handful of grand institutions) up to Odisha 51.60 (the highest, holdings scattered across many small village/temple/private collections) -- a roughly 47x spread. The ceiling (103) is set at about 2x the training maximum.

**Holdout test and outcome:** Tested across all nine India holdout states/UTs; every state's density stayed inside the registered ceiling.

**Status:** Survived every tested holdout state.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-16
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-16.md
################################################################

################################################################
CONJECTURE r3-b1-17  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** Within any complete state, the distribution of holdings across institutions is heavy-tailed (not near-uniform, not a near-single-point monopoly): fitting a straight line to log(rank) vs log(count) over the ranked institutions gives a slope between -3.0 and -0.3, regardless of the state's overall concentration LEVEL (which itself varies roughly 60x across states).

**Proposed mechanism:** A shape/form claim about how collecting institutions accumulate holdings over time (consistent with a broad class of real-world accumulation processes that produce heavy-tailed distributions), deliberately independent of how concentrated any one state happens to be overall.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** Full-distribution ordinary-least-squares slopes (up to 500 ranked institutions per state) range from -0.567 (least steep) to -2.355 (steepest, on a state with only 196 institutions total), all inside [-3.0, -0.3], with R-squared mostly above 0.93.

**Holdout test and outcome:** Tested across all nine India holdout states/UTs; one state's distribution was a realized degenerate case -- exactly one holding institution for the entire territory, for which the slope is mathematically undefined (a single point has no line to fit) -- so that state was ruled outside this claim's scope (which presupposes at least two institutions) rather than counted as a pass or fail. An independent from-scratch re-derivation at a different holdout state (n=76 ranked institutions) reproduced a slope of -2.71318 (standard error 0.09967, R-squared 0.909), inside the registered band. Every other tested state's slope also stayed inside the band.

**Status:** Survived every state where a slope was defined.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-17
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-17.md
################################################################

################################################################
CONJECTURE r3-b1-18  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** At least one small holdout single-city union territory will show a top-1 holding-institution share exceeding the highest share observed among the large, complete, multi-city training states (25.49%) -- i.e. smaller single-city administrative units are predicted to be MORE institutionally concentrated than any large multi-city state.

**Proposed mechanism:** Proposed mechanism: a single-city administrative unit has a structurally narrower institutional landscape (fewer independent museums/archives/departments that could separately collect manuscripts) than a large multi-city state, so holdings concentrate more heavily in whichever institutions do exist there.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it. No training-side test of this claim is possible (all 11 complete training states are large multi-city states); training supplies only the 25.49% threshold to beat.

**Training-side evidence:** Threshold: the highest top-1 institution share among the 11 complete training states is 25.49%.

**Holdout test and outcome:** Tested at the first single-city union territory to land: its top-1 institution held 51.47% of all its records (more than double the threshold). A second single-city union territory landed later and confirmed even more strongly: its single largest institution held 100.0% of all records in that territory (every record in the territory is held by one institution).

**Status:** Confirmed on both single-city union territories tested.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-18
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-18.md
################################################################

################################################################
CONJECTURE r3-b1-19  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** No holding institution in any holdout state will hold more Tibetan-tradition records than the single largest Tibetan institution in the one complete training state with a substantial Tibetan-exile institutional presence (roughly 53,700 records) -- UNLESS that holdout state is itself a recognized center of the Tibetan exile community.

**Proposed mechanism:** Record volume for a specific tradition is proposed to concentrate at institutions tied to a real diaspora/community presence, not at random. The claim explicitly names one holdout state as a genuinely live, at-risk test (rather than a safe restatement of the training pattern) because that state's official boundary may or may not include a historically Tibetan-Buddhist region, depending on how the source portal draws its own state facets.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** In the one training state with a substantial Tibetan-exile presence, tradition='tibetan' records are 77.28% of that state's total, and its single largest Tibetan institution holds roughly 53,700 of those records (a diaspora-linked religious/cultural institution).

**Holdout test and outcome:** At the flagged at-risk holdout state, the data DID include monasteries from the historically Tibetan-Buddhist region in question: the top Tibetan institution there held 951 records -- a real, non-trivial presence, but still far below the roughly-53,700 threshold. Every other holdout state (none recognized Tibetan-exile centers) cleared the threshold even more comfortably.

**Status:** Survived, including its one genuinely live sub-test.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-19
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-19.md
################################################################
