---
conjecture: r3-b1-35
family: codex
model: "gpt-5.6-sol (codex-cli 0.144.0-alpha.4, ChatGPT-subscription auth)"
model_stamp_corrected: "2026-08-07 — archives were first written stamped gpt-5.5 by a runner bug (the stamper read config.toml's default instead of the -m flag actually passed). The calls themselves ran on gpt-5.6-sol throughout; only the label was wrong. Corrected in place, noted here rather than silently."
received: 2026-08-07
template: PROMPT_TEMPLATE_v1.md sha256 4344aabfacfe88362c56d89b6c89d594a7bf3a221e18a16b0e351482447e512f
rivals_parsed: 3
validation: VALID
---

{
  "rivals": [
    {
      "mechanism": "The portal or project distinguishes a manuscript's date from the precision assigned to that date, and list records may contain date text without an explicit precision qualifier. The pipeline may therefore parse dates correctly while legitimately defaulting date_precision to 'unknown' because it does not infer precision from the date text itself.",
      "why_it_explains_the_same_evidence": "A portal-wide metadata convention that omits explicit precision qualifiers from list records would yield exact-zero populated date_precision values in every training and holdout state, regardless of whether dates themselves were harvested. The exceptional records in two other states could have acquired explicit precision metadata through a different cataloguing or enrichment workflow.",
      "named_confounds": [
        "ontology mismatch between date and date precision",
        "absence of explicit precision qualifiers",
        "state-specific metadata enrichment"
      ],
      "discriminating_test": {
        "description": "For retained list-enumeration records in every tested state, inspect the raw list response and harvested intermediate fields for date strings, date ranges, centuries, or normalized date values. The stated mechanism predicts that no date column or value was parsed; this rival predicts that dates are frequently present or parsed while only the separate precision qualifier is absent.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "A substantial number of tested records have harvested or raw list-level dates while date_precision remains uniformly 'unknown', with no explicit precision qualifier available."
      }
    },
    {
      "mechanism": "The list-import transformation may overwrite or default date_precision to 'unknown' after relevant source information has already been retrieved. This would be a mapping or normalization defect, rather than an inability of list-page enumeration to parse a date column.",
      "why_it_explains_the_same_evidence": "If all complete-state walks passed through the same faulty transformation branch, it would deterministically erase populated values across millions of training records and all nine holdouts. Records elsewhere could retain populated precision values because they passed through a detail-enrichment or alternative ingestion branch.",
      "named_confounds": [
        "post-harvest field overwrite",
        "schema-mapping defect",
        "ingestion-path heterogeneity"
      ],
      "discriminating_test": {
        "description": "Re-query a stratified sample of list pages and compare raw responses, parser output before normalization, and final records field by field. Search specifically for explicit precision-bearing source values or non-'unknown' intermediate values that become 'unknown' only during a later transformation.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "Precision-bearing information appears in the raw response or pre-normalization record but is replaced by 'unknown' downstream."
      }
    },
    {
      "mechanism": "The 20 tested states may belong to a shared legacy bulk-ingestion cohort whose source catalogues or import templates never supplied date-precision metadata, while the two states with populated records used a different template or cataloguing workflow. The harvesting method could be capable of preserving precision whenever the portal supplies it.",
      "why_it_explains_the_same_evidence": "State facets would then partition holdings geographically without providing independent variation in the upstream cataloguing process. Uniform absence across all training and holdout states follows from their common ingestion cohort, while the concentration of populated records in two outside states is positive evidence for workflow-specific rather than harvester-wide behavior.",
      "named_confounds": [
        "legacy bulk-import template",
        "administrative ingestion cohort",
        "state facet mistaken for independent cataloguing practice",
        "source-side metadata missingness"
      ],
      "discriminating_test": {
        "description": "Run the identical list-only enumerator and the identical exclusion filter on the two outside states known to contain populated date-precision records, while recording import-template or provenance metadata where available. Compare list-level precision rates across ingestion cohorts rather than merely across state facets.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "Non-'unknown' precision values survive through the same list-only code path in the two outside states and cluster by import template or provenance; the stated list-method mechanism instead predicts that the same enumerator cannot recover them."
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "Because every holdout reused one portal, field definition, exclusion rule, and processing path, the exact-zero replication establishes strong pipeline stability but does not by itself distinguish source-side missingness, semantic non-equivalence, and downstream transformation behavior."
}
