{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007041295,0.0001944391,0.0005372546,0.0002596206,0.00001282738,0.00001095663,0.00007677836,0.0001694678,0.05534262],"category_scores_gemma":[0.0004303473,0.0001522269,0.00009981837,0.0001497352,0.00008301147,0.000007431401,0.00002524526,0.0001180816,0.00003652794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006813517,"about_ca_system_score_gemma":0.00003599976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004454915,"about_ca_topic_score_gemma":0.0008535121,"domain_scores_codex":[0.9987624,0.0004176627,0.0001998215,0.0003003911,0.0001638809,0.0001558407],"domain_scores_gemma":[0.9989344,0.0005221482,0.0001325066,0.0002978714,0.00002232123,0.00009073976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[6.330013e-7,0.00004246308,0.002406304,0.000116184,0.0005842379,0.00001145576,0.000009351488,3.814614e-9,5.44515e-7,0.3268602,0.6591429,0.0108257],"study_design_scores_gemma":[0.0002968788,0.00007017727,0.01273598,0.00012343,0.003538172,0.000008953225,0.00001255061,0.0001133848,0.000006293953,0.4237405,0.5584382,0.0009154429],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[5.495109e-7,0.0003072067,0.5553843,0.000005323076,0.00008270751,0.00005333775,0.00001997836,0.00004004362,0.4441065],"genre_scores_gemma":[0.00002372552,0.00004927318,0.4792741,0.00001943416,0.00002806688,0.00000516565,0.000004127897,0.00007763148,0.5205185],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1007046,"threshold_uncertainty_score":0.9455209,"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."}}