{"id":"W288980890","doi":"10.1137/1.9781611973440.95","title":"Discriminative Density-ratio Estimation","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Discriminative model; Matching (statistics); Computer science; Covariate; Density estimation; Artificial intelligence; Class (philosophy); Regression; Focus (optics); Machine learning; Pattern recognition (psychology); Mathematics; Statistics; Estimator","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":[0.0001554069,0.0001507634,0.0001543175,0.00009733275,0.000131835,0.0002424989,0.0007018589,0.0001489782,0.0000199945],"category_scores_gemma":[0.00002204847,0.0001368537,0.00008154534,0.0001158421,0.00003234125,0.0001510877,0.0009440057,0.0002613548,0.0001111395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005672795,"about_ca_system_score_gemma":0.00005197213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006337898,"about_ca_topic_score_gemma":0.000009773312,"domain_scores_codex":[0.9990242,0.00004125202,0.0002049543,0.0004541625,0.000160127,0.0001153593],"domain_scores_gemma":[0.9987873,0.00003937416,0.0001596929,0.0008328775,0.0001278494,0.00005286626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[4.700668e-7,0.00003055653,0.00001021928,0.00002496849,0.000009463023,5.118197e-7,0.0001933238,0.001284473,0.000110599,0.9031163,0.003415656,0.09180339],"study_design_scores_gemma":[0.00002868978,0.00002141427,0.0005489156,0.00001687051,0.000007578415,0.000003583717,0.000005784174,0.7872016,0.01298537,0.1976888,0.00130152,0.0001897937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008488197,0.000006203602,0.9832936,0.001604756,0.0001555905,0.0003021897,0.000001522827,0.0008531912,0.01293413],"genre_scores_gemma":[0.6243742,0.000004950386,0.3740886,0.0002282226,0.00004174248,0.0001387528,0.00001216373,0.000005644508,0.001105802],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7859172,"threshold_uncertainty_score":0.558073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01826353425139323,"score_gpt":0.2804759159656743,"score_spread":0.2622123817142811,"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."}}