Combinations (cross-domain hypotheses) by status
| id | status | bridge | claim_a | claim_b | statement | falsify |
|---|---|---|---|---|---|---|
| ts-combo-contested-by-evidence-source | ready_to_test | ts-claim-cf1-contested-claim-level | ts-claim-rc1-contested-fraction-by-evidence-source | Contestedness is a property of the evidence-gathering process, not of science: retrieval corpora ~20%, replication corpora ~40–60%; the retrieval number measures what annotators find, the replication number measures what experiments find. | A replication corpus re-scored with retrieval-style evidence (papers citing the original) also gives ~20%, or a retrieval corpus restricted to replication studies gives ~40%+. | |
| ts-combo-contested-claims-claim-level | ready_to_test | within-claim polarity concordance: mixed SUPPORTS/REFUTES evidence vs the independence baseline | ts-claim-cf1-contested-claim-level | ts-claim-so1-contested-after-open-retrieval | In open-retrieval claim-verification corpora the contested fraction among claims with two or more polar evidence documents is about 0.2, independent of the number of documents and of domain; closed citation-built corpora show about 0 by construction, not because scientific claims are uncontested. | A fourth open-retrieval corpus with >=2 polar documents per claim whose contested fraction falls outside 12-28% or whose trend in k has z > 1.6; or SciFact-Open's contested fraction rising above 35% as evidence per claim grows; or independence overprediction below 2x in any such corpus. |
| ts-combo-listwise-collapse-is-noisy-argmax | ready_to_test | noisy argmax: probability that the best-by-noisy-score is the true best decreases with N at fixed pairwise accuracy | ts-claim-mg1-noisy-tournament-selection | ts-claim-z1-listwise-collapse-global-discrimination | Accuracy@1 of an LLM pairwise judge over N unexecuted ML candidates equals the noisy-argmax accuracy of a Thurstone case-V comparator at the judge's pairwise accuracy; no additional listwise 'global discrimination' deficit is needed to explain Zheng Table 3, and the same arithmetic bounds RPM child-selection as N grows. | Pre-registered: at p=0.59 the model predicts Acc@1 = 0.221 (N=8), 0.191 (N=10), 0.146 (N=15). A re-run of Zheng's ranking subset at those N with Acc@1 more than 2 SE below these values falsifies the combination; matching values within 2 SE support it. Second test: an RPM/AIRA-dojo tournament with N children whose selection accuracy tracks these curves. |