{"id":"W2157457162","doi":"10.1017/s0266466604205035","title":"NONPARAMETRIC IDENTIFICATION OF LATENT COMPETING RISKS MODELS","year":2004,"lang":"en","type":"article","venue":"Econometric Theory","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Nonparametric statistics; Identification (biology); Conditional independence; Econometrics; Independence (probability theory); Mathematics; Latent variable; Subject matter; Conditional expectation; Statistics; Political science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002160138,0.0001058055,0.0002860131,0.0006652079,0.00004923852,0.00002414046,0.0002056413,0.00006012951,0.000257192],"category_scores_gemma":[0.005008643,0.00009725064,0.00007874351,0.001353194,0.0000791176,0.00009655896,0.00005089226,0.0001155143,0.00005580137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007768226,"about_ca_system_score_gemma":0.00003065723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000184312,"about_ca_topic_score_gemma":4.031292e-7,"domain_scores_codex":[0.9987434,0.0001288147,0.0006229647,0.0002089816,0.0001201911,0.0001756305],"domain_scores_gemma":[0.9959564,0.003218872,0.0003612463,0.0003115966,0.00008635441,0.00006556665],"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":[0.000009327025,0.0001157095,0.0002229181,0.0000504278,0.00002418717,6.284887e-7,0.0001753199,0.001084381,0.00006428346,0.9625465,0.000004648966,0.03570165],"study_design_scores_gemma":[0.0002901859,0.00004594399,0.00563905,0.00001950823,0.00002924102,0.000002038046,0.00008630371,0.003531785,0.002288254,0.9879507,0.000006287253,0.0001107039],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3405574,0.0002175216,0.6527353,0.0000172303,0.0001292427,0.0001338336,0.00002493992,0.00003059928,0.006153934],"genre_scores_gemma":[0.8715895,0.00003729495,0.1282353,0.00001636541,0.00002919239,0.00001122011,0.000001834567,0.00001355682,0.00006565004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5310321,"threshold_uncertainty_score":0.5996175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1732140105350803,"score_gpt":0.3741523291083121,"score_spread":0.2009383185732319,"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."}}