{"id":"W2137744864","doi":"","title":"Probabilistic n-Choose-k Models for Classification and Ranking","year":2012,"lang":"en","type":"article","venue":"Digital Access to Scholarship at Harvard (DASH) (Harvard University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Categorical variable; Prior probability; Probabilistic logic; Computer science; Poisson distribution; Inference; Artificial intelligence; Mathematics; Ranking (information retrieval); Machine learning; Pattern recognition (psychology); Statistics; Bayesian probability","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":[0.01153466,0.002041476,0.004321574,0.004360235,0.003339861,0.004617747,0.01094349,0.005580298,0.01469011],"category_scores_gemma":[0.03814149,0.001713888,0.002969142,0.00708155,0.005595621,0.01145842,0.003586273,0.006768811,0.004167085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005282226,"about_ca_system_score_gemma":0.002136766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01314878,"about_ca_topic_score_gemma":0.02321924,"domain_scores_codex":[0.9923149,0.004366538,0.0003400974,0.001293914,0.001060685,0.0006239053],"domain_scores_gemma":[0.9742162,0.01937624,0.002075859,0.002377025,0.001375235,0.0005795136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001710939,0.0001230333,0.002256912,0.0002054535,0.00009958378,0.000185132,0.000402352,0.1400529,0.0001617522,0.8169547,0.007841893,0.03154516],"study_design_scores_gemma":[0.00004031283,0.00001948252,0.0002673402,0.00002928751,0.00002203683,0.00006966269,0.00004270294,0.4004478,0.00006587937,0.5968656,0.002094206,0.00003561681],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01241061,0.001611045,0.9743095,0.003572386,0.0001587444,0.0001933267,0.001465393,0.0006726229,0.005606332],"genre_scores_gemma":[0.6221159,0.004525481,0.3268264,0.001986982,0.001614656,0.002453425,0.00429266,0.0005043643,0.03568028],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01469011,"threshold_uncertainty_score":0.06100184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0850773780527327,"score_gpt":0.2743933822400066,"score_spread":0.1893160041872739,"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."}}