{"id":"W2144017161","doi":"10.1023/a:1015853905091","title":"Semiparametric Inference Methods for General Time Scale Models","year":2002,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Estimator; Inference; Mathematics; Econometrics; Scale (ratio); Semiparametric model; Statistics; Semiparametric regression; Rank (graph theory); Computer science; Artificial intelligence; Combinatorics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001900489,0.000257328,0.0008447649,0.0005087814,0.0001334864,0.0001288262,0.001126465,0.0001421041,0.003719735],"category_scores_gemma":[0.007484465,0.0002208821,0.0002379225,0.002570059,0.00008942578,0.0002865369,0.0004044782,0.0001658424,0.000170166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002917921,"about_ca_system_score_gemma":0.00001796204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006762604,"about_ca_topic_score_gemma":0.000006106085,"domain_scores_codex":[0.9974007,0.0004605505,0.0006369415,0.0007653798,0.0002981818,0.0004382571],"domain_scores_gemma":[0.9905478,0.006746993,0.000216871,0.002086936,0.0001813764,0.0002199974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005520714,0.001073104,0.001095208,0.0002411491,0.005520162,0.00000676333,0.0004786871,0.001864823,0.001348046,0.1931243,0.09731437,0.6978782],"study_design_scores_gemma":[0.0001549362,0.00003511995,0.00006883498,0.000006838442,0.002167066,8.785889e-7,0.000005586248,0.792852,0.0001282317,0.2024187,0.001922545,0.0002392195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009401412,0.0002127928,0.9934397,0.0001345634,0.0000407386,0.0002369189,0.00194653,0.00009357292,0.002955016],"genre_scores_gemma":[0.002101758,0.00006290035,0.9933823,0.0001665585,0.0001073245,0.00004693936,0.0005496352,0.00002807957,0.003554533],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7909872,"threshold_uncertainty_score":0.997191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2459689835820602,"score_gpt":0.472600993780796,"score_spread":0.2266320101987357,"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."}}