{"id":"W1524513824","doi":"10.1002/0470867205.ch15","title":"Event History Analysis and Longitudinal Surveys","year":2003,"lang":"en","type":"other","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Event (particle physics); Inference; Survival analysis; Observational study; Statistics; Event data; Duration (music); Econometrics; Variance (accounting); Computer science; Data science; History; Data mining; Mathematics; Artificial intelligence; Art","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00674042,0.0008942679,0.001146961,0.003213604,0.0005168525,0.002066209,0.001208367,0.001412168,0.02581461],"category_scores_gemma":[0.03230519,0.0006067878,0.0008627521,0.006382231,0.001275555,0.00401112,0.001211636,0.002561867,0.003915614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001181523,"about_ca_system_score_gemma":0.001515553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0043432,"about_ca_topic_score_gemma":0.002739217,"domain_scores_codex":[0.9962709,0.002551646,0.0001682244,0.0004163244,0.0005007914,0.00009210053],"domain_scores_gemma":[0.9786077,0.01724262,0.001201482,0.001741127,0.0009732877,0.000233861],"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":[0.00002647255,0.00004623562,0.002767229,0.0003633894,0.00009503957,0.0000856465,0.0002675362,0.008452551,0.00009642084,0.8011261,0.03461678,0.1520566],"study_design_scores_gemma":[0.00001239403,0.00001660243,0.002455477,0.0001514584,0.00002508729,0.0001003035,0.0000976793,0.01756129,0.00007244159,0.9095619,0.06992216,0.00002321884],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003743833,0.02091507,0.9214789,0.006531354,0.0006950842,0.0001851469,0.002741022,0.0007565754,0.04295311],"genre_scores_gemma":[0.2537417,0.08951032,0.517979,0.003961307,0.004805253,0.001975911,0.01061878,0.000717625,0.1166901],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02581461,"threshold_uncertainty_score":0.08635849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0824464211909022,"score_gpt":0.3657364947425574,"score_spread":0.2832900735516553,"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."}}