{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006843463,0.00007546512,0.00007176186,0.00004563871,0.0001093728,0.0001449327,0.0003239177,0.00002335945,0.000002630656],"category_scores_gemma":[0.0001655634,0.00005565847,0.00001530358,0.0001968354,0.00003085332,0.001019141,0.00009337469,0.0001111153,0.00001241211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002724902,"about_ca_system_score_gemma":0.00001502875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002621842,"about_ca_topic_score_gemma":0.000005032998,"domain_scores_codex":[0.9990981,0.0000688309,0.0001113447,0.0001807488,0.0001790201,0.0003619423],"domain_scores_gemma":[0.9993573,0.00008190508,0.00008185668,0.0003541475,0.0000310081,0.00009376449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000107028,0.00004829946,0.6400174,0.00003394717,0.00001455747,0.000001136457,0.001580185,0.00007396164,0.000004503418,0.03471195,0.0001261397,0.3233772],"study_design_scores_gemma":[0.0002242593,0.00001079881,0.9821944,0.000008348465,0.000005949384,0.00002605209,0.00004191622,0.006801461,0.00009543134,0.0003639232,0.01010031,0.0001271979],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5660138,0.0003751795,0.4039195,0.003671964,0.0005705568,0.001010949,0.000004078925,0.00107963,0.02335437],"genre_scores_gemma":[0.9933653,0.000002514717,0.005558905,0.00008740175,0.0001401542,0.0005241159,0.000004704686,0.00000590025,0.0003109556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4273516,"threshold_uncertainty_score":0.2269687,"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."}}