{"id":"W2107103101","doi":"","title":"Predicting accurate probabilities with a ranking loss.","year":2012,"lang":"en","type":"article","venue":"PubMed","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Ranking (information retrieval); Computer science; Machine learning; Artificial intelligence; Isotonic regression; Logistic regression; Set (abstract data type); Regression; Parametric statistics; Data mining; Statistics; Mathematics","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":[0.006206172,0.001390609,0.001369212,0.003237158,0.0006422694,0.001980689,0.002333928,0.001632995,0.006576435],"category_scores_gemma":[0.03264084,0.0004083303,0.001088504,0.003161672,0.0006644736,0.002647755,0.001634974,0.001974066,0.007258241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006854796,"about_ca_system_score_gemma":0.001920702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00203632,"about_ca_topic_score_gemma":0.004806668,"domain_scores_codex":[0.9950762,0.002088303,0.0003604712,0.0008273548,0.001380859,0.0002667079],"domain_scores_gemma":[0.9894077,0.005383119,0.0009021722,0.00256281,0.001496665,0.0002476362],"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":[0.0009443343,0.0006598479,0.01380575,0.0004683652,0.0002940356,0.0002778698,0.00008324772,0.1576529,0.006441051,0.01428966,0.03850803,0.7665749],"study_design_scores_gemma":[0.00004237059,0.0002308845,0.001995563,0.00003203844,0.00005367061,0.0003280645,0.00003539615,0.9758998,0.004954657,0.01255105,0.00384238,0.00003431687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05231544,0.001561371,0.9302241,0.001020159,0.0003342009,0.0003666104,0.001767537,0.005934205,0.006476457],"genre_scores_gemma":[0.647832,0.0006373039,0.3281651,0.0005567757,0.0005099661,0.0005585056,0.00640914,0.0005223784,0.01480887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006576435,"threshold_uncertainty_score":0.03282177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02706841915597657,"score_gpt":0.2215533239881784,"score_spread":0.1944849048322018,"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."}}