Resolution: Supported
Caveats: Calibration verdict, not a novelty claim (triage: adjacent — Cisne 2005 and the unseen-species literature model manuscript transmission demographically). Nuance that must survive into any narrative: the heavy-tail FAMILY is confirmed in both datasets, but the pure power law wins only in DBBE (alpha=2.37, inside the registered (1.5,4.0) band); in Pinakes the discretized lognormal beats the zeta by ~2,009 AIC, which is consistent with preferential-attachment-with-death or proportional-growth variants but NOT with a pure Yule process — the conjecture's specific 'power-law copy counts' wording is only partially vindicated. The superlinear size-biased survival half of the harvest conjecture is untested (needs medieval catalogue-vs-extant joins). Coverage caveats from the extracts apply (Byzantine Greek catalogue; book-epigram genre scope). Survival bias: these are EXTANT witness counts, i.e. the conjecture's process convolved with loss, not production counts.
In both in-house works-by-witnesses datasets (Pinakes Greek works: 21,513 works / 245,592 witness links; DBBE Byzantine epigram type-groups: 4,898 groups / 12,123 witnesses), the copies-per-work distribution is heavy-tailed in the preferential-attachment family rather than thin-tailed — the distributional signature the harvest conjecture requires. Calibration test only: triage is 'adjacent' (Cisne 2005; unseen-species literature), so no novelty is claimed, and the superlinear-survival half of the conjecture is NOT testable with these data.
Resolution criteria: Fit three models to each dataset's copies-per-work counts by maximum likelihood: discrete power law (zeta, xmin=1), discretized lognormal, and geometric. SUPPORTED if min(AIC_powerlaw, AIC_lognormal) < AIC_geometric - 10 in BOTH datasets AND works with k >= 10 witnesses comprise >= 0.5% of works in both. KILLED if the geometric model is within 10 AIC of the best model in EITHER dataset. Otherwise INCONCLUSIVE. If the power law wins, report its exponent; an exponent outside (1.5, 4.0) counts against the preferential-attachment reading in the narrative but does not alone kill.
Known priors disclosure: The registrant has seen the Phase-A extractor summary counts only (Pinakes: 21,513 works, 245,592 witnesses, f1=7,545 singleton works; DBBE: 4,898 groups, 12,123 witnesses) and holds the general bibliometric prior that copy-count distributions are typically heavy-tailed — which is precisely why this is registered as calibration, not discovery. The registrant has NOT seen either distribution's shape, tail mass, or any fitted model.
Maximum-likelihood fits of three discrete models (zeta power law xmin=1; discretized lognormal; geometric) to each f_k distribution, compared by AIC, exactly as pre-registered. scipy-free implementation (Euler-Maclaurin zeta, erf-based normal CDF, golden-section/coordinate optimization); script preserved with the artifact.
Dataset: In-house copies-per-work distributions: Pinakes Greek works x witnesses (21,513 works, 245,592 witness links; Byzantine Greek catalogue coverage) and DBBE book-epigram type groups (4,898 groups, 12,123 witnesses; genre-scoped). Both from committed Phase-A extract artifacts; no new ingestion.
computed 2026-07-04