{"id":"W2182669574","doi":"10.1007/s10985-015-9352-x","title":"A case-base sampling method for estimating recurrent event intensities","year":2015,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Sampling (signal processing); Context (archaeology); Event (particle physics); Proportional hazards model; Outcome (game theory); Logistic regression; Mathematics; Hazard; Econometrics; Computer science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003570335,0.0002126416,0.0006798296,0.000225681,0.0001353753,0.0001251326,0.0004286902,0.00006955896,0.0001173621],"category_scores_gemma":[0.01877631,0.0001787192,0.0001769582,0.0006228957,0.00004874909,0.0001478589,0.0003449084,0.0001476094,0.00001436726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005624237,"about_ca_system_score_gemma":0.00008244944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002763661,"about_ca_topic_score_gemma":0.0001028389,"domain_scores_codex":[0.9978389,0.0003403855,0.0006347051,0.0005738835,0.0002919785,0.0003201367],"domain_scores_gemma":[0.9929637,0.004928733,0.0002658427,0.001255777,0.0003342873,0.0002516418],"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":[0.0002884087,0.0005717777,0.0004063384,0.0007599409,0.006048171,0.0006068611,0.003190821,0.001173001,0.00007907329,0.206262,0.04193068,0.7386829],"study_design_scores_gemma":[0.0002184263,0.00006254098,0.000004289207,0.00004408675,0.003265065,0.0001009413,0.0006912391,0.7871654,0.00003218331,0.2077673,0.000439855,0.0002086626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002753648,0.00005557458,0.9962059,0.0002509083,0.0001601989,0.0002274322,0.002677909,0.00007341065,0.00007324896],"genre_scores_gemma":[0.001998024,0.000001347601,0.9967787,0.00009341393,0.0002060261,0.0000430926,0.0008105348,0.0000228697,0.00004594734],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7859924,"threshold_uncertainty_score":0.989489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3257947847264668,"score_gpt":0.491397282387292,"score_spread":0.1656024976608251,"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."}}