{"id":"W2156604399","doi":"","title":"Bayesian Nonparametric Modeling of Suicide Attempts","year":2012,"lang":"en","type":"article","venue":"Cambridge University Engineering Department Publications Database","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Columbia College","funders":"","keywords":"Computer science; Nonparametric statistics; Generative model; Multinomial logistic regression; Multinomial distribution; Population; Econometrics; Sample (material); Gibbs sampling; Discrete choice; Laplace's method; Bayesian probability; Artificial intelligence; Machine learning; Data mining; Mathematics; Generative grammar","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":[0.009946941,0.0006705751,0.001907824,0.001412325,0.0007960132,0.001930624,0.00379917,0.001918974,0.003349879],"category_scores_gemma":[0.03804119,0.0009511281,0.001436749,0.001767146,0.001128279,0.002142158,0.001604346,0.002494222,0.0007550223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001735443,"about_ca_system_score_gemma":0.001208876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01954701,"about_ca_topic_score_gemma":0.01615816,"domain_scores_codex":[0.9966929,0.00231206,0.0001125807,0.0003478302,0.0003621429,0.0001724043],"domain_scores_gemma":[0.9802135,0.01657483,0.000810466,0.001190463,0.0009439096,0.0002668532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003971994,0.0002500801,0.01279651,0.0001330201,0.0002048673,0.0002372114,0.00069368,0.7695431,0.0004512614,0.1481193,0.005002101,0.06217156],"study_design_scores_gemma":[0.00001806946,0.000008974765,0.0008487091,0.00001159783,0.000008081608,0.00002422163,0.00002696437,0.9749607,0.00005144835,0.02359736,0.0004323509,0.00001158371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1837704,0.001038511,0.8059888,0.001666815,0.00009157258,0.0002775088,0.002061591,0.0007155081,0.004389186],"genre_scores_gemma":[0.878834,0.0008412505,0.111477,0.0001995719,0.0001419104,0.0005654585,0.00268021,0.0001509678,0.005109469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01954701,"threshold_uncertainty_score":0.05260509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0223815379823416,"score_gpt":0.2372032337244319,"score_spread":0.2148216957420903,"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."}}