{"id":"W4411984890","doi":"10.1080/01621459.2025.2519814","title":"Adaptive Selection for False Discovery Rate Control Leveraging Symmetry","year":2025,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Key Research and Development Program of China; Alberta Machine Intelligence Institute","keywords":"False discovery rate; Selection (genetic algorithm); Computer science; Econometrics; Mathematics; Statistics; Artificial intelligence; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.03628202,0.001429549,0.001967745,0.001575091,0.000737528,0.00154899,0.002999716,0.001424935,0.001699154],"category_scores_gemma":[0.09984595,0.0005651477,0.001324206,0.001756699,0.003494608,0.001984672,0.003011878,0.002845048,0.0006291617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009009238,"about_ca_system_score_gemma":0.0031552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007743122,"about_ca_topic_score_gemma":0.0005550883,"domain_scores_codex":[0.9735649,0.01955521,0.001054708,0.002233341,0.003133631,0.0004581645],"domain_scores_gemma":[0.9161637,0.06784561,0.0042126,0.006890411,0.00412818,0.0007593536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001968945,0.0003869959,0.01308434,0.0006363688,0.0008793349,0.001137595,0.000803653,0.3150131,0.02911567,0.233559,0.005802869,0.3976121],"study_design_scores_gemma":[0.0002865726,0.0003835761,0.00124012,0.00003908411,0.00008595441,0.0002866235,0.0000443531,0.9009339,0.006863008,0.08814321,0.001639881,0.00005375946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00418719,0.0001260901,0.9949107,0.0001295886,0.00003204465,0.00006852987,0.00002775135,0.000242761,0.0002753914],"genre_scores_gemma":[0.4916598,0.0004268745,0.5045424,0.0006075755,0.0003469569,0.0008922533,0.0002494058,0.0002433786,0.001031341],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03628202,"threshold_uncertainty_score":0.1918799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1556518943717469,"score_gpt":0.4870110114623158,"score_spread":0.3313591170905689,"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."}}