{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05014219,0.001114016,0.002840909,0.002413346,0.001100492,0.00480646,0.003006449,0.003268602,0.003319053],"category_scores_gemma":[0.2516118,0.0011653,0.0009569314,0.001324592,0.006225778,0.006769738,0.002974338,0.004417495,0.0006353584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00263787,"about_ca_system_score_gemma":0.003605727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001013825,"about_ca_topic_score_gemma":0.001049148,"domain_scores_codex":[0.9549102,0.03061355,0.0021765,0.005603041,0.005300499,0.001396225],"domain_scores_gemma":[0.659394,0.3126935,0.008852322,0.01147425,0.005424807,0.002161062],"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":[0.0004052219,0.0002094457,0.003401395,0.0002789507,0.0002044492,0.0001473647,0.0003742621,0.08943389,0.001131281,0.8507225,0.002485765,0.05120549],"study_design_scores_gemma":[0.0001971125,0.0001407091,0.0006107956,0.00008763489,0.00003571182,0.00006920435,0.00006551918,0.1968484,0.0008189523,0.7998738,0.00122141,0.00003062984],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06810474,0.0008729561,0.9133021,0.004063368,0.00013541,0.000789921,0.0004036508,0.00026391,0.01206393],"genre_scores_gemma":[0.6225803,0.000845054,0.3688547,0.001398465,0.0002419688,0.002268822,0.0004433266,0.0001307179,0.003236744],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05014219,"threshold_uncertainty_score":0.2651804,"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."}}