{"id":"W4237053211","doi":"10.31219/osf.io/upb4w","title":"Knowledge Attributions in Iterated Fake Barn Cases","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Attribution; Barn; Iterated function; Object (grammar); Cognition; Computer science; Psychology; Social psychology; Artificial intelligence; Mathematics; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0002035952,0.000194811,0.0003502367,0.00009052623,0.0003059204,0.000144196,0.0003241745,0.000229265,0.0006252067],"category_scores_gemma":[0.0001598486,0.0002137882,0.0001095546,0.0002265388,0.0002502075,0.00008933242,0.0008912229,0.0003941199,0.0004567938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007176886,"about_ca_system_score_gemma":0.0003906691,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02028953,"about_ca_topic_score_gemma":0.08358839,"domain_scores_codex":[0.9986646,0.0001556947,0.0003233696,0.0004476085,0.00009356863,0.0003151727],"domain_scores_gemma":[0.9994522,0.0001072959,0.00007751604,0.0001497927,0.00008065195,0.0001325897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005038113,0.001253664,0.2325264,0.0001227428,0.0001802454,0.0002987335,0.1696326,0.0001192446,0.00128695,0.5445938,0.04753584,0.002399438],"study_design_scores_gemma":[0.004274936,0.0005656404,0.1189026,0.00138227,0.0004208236,0.00001382475,0.223628,0.002070047,0.01050808,0.04980582,0.5794909,0.00893704],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6815876,0.002011013,0.0000338135,0.00523717,0.001351256,0.0009210413,0.0002529486,0.0003270936,0.3082781],"genre_scores_gemma":[0.9964193,0.0002451567,0.0002233113,0.0001243772,0.0001728452,0.0001453658,0.00008987645,0.0000138802,0.002565848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5319551,"threshold_uncertainty_score":0.9862344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1950030073717167,"score_gpt":0.4292060957508176,"score_spread":0.2342030883791009,"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."}}