{"id":"W2963426032","doi":"10.1080/01621459.2018.1527700","title":"FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control","year":2018,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences","keywords":"False discovery rate; Multiple comparisons problem; Estimator; Computer science; Robust statistics; Covariance; Normality; Inference; Statistical hypothesis testing; Mathematics; Statistics; Data mining; Econometrics; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00307253,0.0002614844,0.001106685,0.00007398003,0.0002144705,0.0001568323,0.000451622,0.00008911125,0.0000464864],"category_scores_gemma":[0.3019197,0.0001509337,0.0001705546,0.0006256381,0.0005770574,0.0002102447,0.00008304285,0.0006541316,0.00001126038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004991362,"about_ca_system_score_gemma":0.0001803082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005367878,"about_ca_topic_score_gemma":0.000029169,"domain_scores_codex":[0.9948978,0.00173517,0.001415823,0.0002592196,0.001210243,0.0004817868],"domain_scores_gemma":[0.896529,0.09751185,0.004295876,0.0003190894,0.001157735,0.0001864634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004531723,0.001754289,0.8556944,0.0002163671,0.002337326,0.0001032783,0.0005952912,0.0003159852,0.005432007,0.03922273,0.01164111,0.07815549],"study_design_scores_gemma":[0.00629412,0.003413219,0.5453463,0.0004202878,0.001363642,0.00008265702,0.000388738,0.01544384,0.0009033973,0.4254408,0.0002200807,0.0006829561],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1637233,0.000004993369,0.833935,0.0008422009,0.0004787159,0.0003372485,0.0004601171,0.00003733412,0.0001810771],"genre_scores_gemma":[0.5211197,0.000001997806,0.4779426,0.000297773,0.0005240399,0.000004906469,4.548617e-7,0.00003102289,0.0000775369],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3862181,"threshold_uncertainty_score":0.7039605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2885035623946631,"score_gpt":0.4485218103862023,"score_spread":0.1600182479915391,"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."}}