{"id":"W4396711969","doi":"10.3982/te4840","title":"Optimal sample sizes and statistical decision rules","year":2024,"lang":"en","type":"article","venue":"Theoretical Economics","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Sample (material); Decision rule; Computer science; Sample size determination; Econometrics; Statistics; Mathematics; Artificial intelligence; Chemistry","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":["insufficient_payload"],"category_scores_codex":[0.001835847,0.0001218958,0.000222946,0.00009391631,0.0001176349,0.0005898148,0.0003518165,0.00007449226,0.005777529],"category_scores_gemma":[0.002452129,0.00008869454,0.00005792526,0.000110691,0.001134046,0.0001571685,0.0001893786,0.000144133,0.00193742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002108299,"about_ca_system_score_gemma":0.00004484469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002190292,"about_ca_topic_score_gemma":0.000001773001,"domain_scores_codex":[0.9986283,0.00008576856,0.0004391866,0.000484553,0.0001479312,0.0002143049],"domain_scores_gemma":[0.9834527,0.01591944,0.00002954757,0.0003720887,0.00003370169,0.0001924607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003776081,0.00001270439,0.00002109772,0.000001765712,0.000006046198,0.000001725594,0.00009103385,0.0001082972,0.000016394,0.8583637,0.0004699246,0.1408695],"study_design_scores_gemma":[0.00008119467,0.00003929216,0.0004534644,0.000007419082,0.00001209608,0.00001571467,0.0001398397,0.05033726,0.00008250471,0.9303864,0.01833622,0.0001086601],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6882495,0.0001199638,0.3018512,0.001165141,0.0001454686,0.00008659324,0.0003216894,0.00006237184,0.007998093],"genre_scores_gemma":[0.9548719,0.00007155985,0.04466944,0.0001620773,0.00009731172,0.000010811,0.00001015878,0.00001404007,0.00009270773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2666224,"threshold_uncertainty_score":0.9988397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03707866861675208,"score_gpt":0.3537423951652954,"score_spread":0.3166637265485434,"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."}}