{"id":"W3162211911","doi":"10.31234/osf.io/urgtn","title":"Z-Curve 2.0: Estimating Replication Rates and Discovery Rates","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Replication (statistics); Confidence interval; False discovery rate; Selection (genetic algorithm); Statistics; False positive paradox; Multiple comparisons problem; Computer science; Range (aeronautics); Extension (predicate logic); Econometrics; Mathematics; Artificial intelligence; Biology; Engineering","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2328074,0.004481846,0.005155711,0.01106481,0.001879815,0.008602342,0.007077645,0.004929001,0.03566558],"category_scores_gemma":[0.643773,0.004535838,0.00940475,0.01125407,0.004653601,0.006578858,0.008604742,0.008218756,0.0139045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002702303,"about_ca_system_score_gemma":0.009815938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003138893,"about_ca_topic_score_gemma":0.002333072,"domain_scores_codex":[0.805907,0.1480033,0.01258367,0.01422541,0.01765917,0.001621371],"domain_scores_gemma":[0.4202949,0.4752117,0.02357792,0.05487964,0.0242936,0.001742145],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004314695,0.0003193584,0.03995289,0.01306767,0.01241681,0.0009644091,0.004700475,0.04445198,0.00495089,0.1561604,0.1398528,0.5788476],"study_design_scores_gemma":[0.003231082,0.001174162,0.02454758,0.003635116,0.005127983,0.002165925,0.0006090191,0.1890622,0.01798762,0.4195101,0.3319316,0.001017604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003566269,0.002059492,0.9623961,0.001197076,0.0006439755,0.00295008,0.007957763,0.01520465,0.004024634],"genre_scores_gemma":[0.03959613,0.001324465,0.9216734,0.0007238592,0.0003306076,0.01636627,0.005220203,0.01220075,0.002564315],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7671926,"threshold_uncertainty_score":0.946085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6573930739385597,"score_gpt":0.6101719849647902,"score_spread":0.04722108897376953,"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."}}