{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004433525,0.0001570209,0.0001790025,0.0006443391,0.0001005917,0.00004243415,0.0007606864,0.00005384408,0.000005148883],"category_scores_gemma":[0.0001109341,0.0001812077,0.00008402109,0.001402073,0.00001989377,0.001757358,0.0003222087,0.0001043181,0.00001377508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000128876,"about_ca_system_score_gemma":0.00007026651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003125159,"about_ca_topic_score_gemma":7.588305e-7,"domain_scores_codex":[0.9988592,0.00005209358,0.0001975867,0.0002923091,0.0002110089,0.0003878541],"domain_scores_gemma":[0.9983915,0.00009651086,0.00008151666,0.0009977749,0.0001301624,0.0003024965],"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":[0.000004667023,0.0003127355,0.001582095,0.0000604863,0.00007512759,0.000005666097,0.00007450324,0.00735003,0.001166474,0.9819278,0.004982772,0.00245769],"study_design_scores_gemma":[0.0002813193,0.00001607203,0.0009961275,0.00002323693,0.00003907002,0.00001726348,0.00001024687,0.9806098,0.001161677,0.000009965708,0.01656108,0.0002741825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007500955,0.0002641062,0.9895854,0.0002475664,0.0001761237,0.0001931527,0.0001313722,0.0002251653,0.001676152],"genre_scores_gemma":[0.5444809,0.00003536819,0.4549687,0.00002947539,0.00003534481,0.000003030398,0.0001442454,0.000009110478,0.0002938655],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9819178,"threshold_uncertainty_score":0.7389438,"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."}}