{"id":"W3162053605","doi":"10.31234/osf.io/7xg8j","title":"Coherence of probability judgments from uncertain evidence: Does ACH help?","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Probabilistic logic; Credibility; Coherence (philosophical gambling strategy); Weighting; Bayesian probability; Empirical evidence; Psychology; Reliability (semiconductor); Cognitive psychology; Social psychology; Computer science; Econometrics; Statistics; Artificial intelligence; Mathematics; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002130104,0.0002762813,0.000653519,0.00008817668,0.00008524499,0.0002370745,0.002958378,0.0002921156,0.002061285],"category_scores_gemma":[0.005728686,0.0001634243,0.0002526683,0.0005537634,0.0002805225,0.0001290442,0.002798834,0.0005129366,0.0001379028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007888689,"about_ca_system_score_gemma":0.0003372261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003192625,"about_ca_topic_score_gemma":0.0001649702,"domain_scores_codex":[0.9952467,0.0002345627,0.001290788,0.0013853,0.00162844,0.0002141967],"domain_scores_gemma":[0.9940981,0.002084607,0.0008614109,0.002124367,0.0006775007,0.0001540527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003706877,0.001311399,0.2576207,0.0007239589,0.0002903247,0.00001429643,0.003828415,0.002535251,0.024214,0.03682123,0.4622843,0.2099855],"study_design_scores_gemma":[0.00005578157,0.00005215503,0.00600984,0.0003489468,0.0000238784,2.536927e-7,0.0001407575,0.004746023,0.01248068,0.9740688,0.001838759,0.0002341566],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7879481,0.0004267918,0.1706383,0.02170958,0.000791089,0.004345702,0.001385031,0.0007733657,0.01198206],"genre_scores_gemma":[0.8433323,0.00002443959,0.1552269,0.0001703615,0.00008154794,0.0002646799,0.00003057622,0.00001160635,0.0008575462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9372475,"threshold_uncertainty_score":0.9988509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4596450484671394,"score_gpt":0.462150808509261,"score_spread":0.002505760042121608,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}