{"id":"W1598185421","doi":"","title":"INFERENCE FOR A GAMMA STEP-STRESS MODEL UNDER CENSORING","year":2012,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McMaster University","keywords":"Censoring (clinical trials); Inference; Econometrics; Computer science; Artificial intelligence; Statistics; Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006140143,0.0003044917,0.0003232224,0.0001053707,0.0002255866,0.0000558633,0.0003131466,0.0002941284,0.01940495],"category_scores_gemma":[0.0001607079,0.0003474328,0.0001505903,0.000251682,0.00004198527,0.0001736974,0.00004317388,0.0002309765,0.00007665977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001877473,"about_ca_system_score_gemma":0.0001126468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001855406,"about_ca_topic_score_gemma":0.0002547144,"domain_scores_codex":[0.9987362,0.00003547617,0.0002749179,0.0003600223,0.000225795,0.0003675864],"domain_scores_gemma":[0.9984707,0.0005254078,0.0002616588,0.0002887831,0.0002680962,0.0001853761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008193596,0.0001577181,0.00003280865,0.0005054043,0.00006284727,0.000001448396,0.0002632647,0.0003163222,0.0001157772,0.9582379,0.002024326,0.03820028],"study_design_scores_gemma":[0.006691342,0.0001757016,0.006391877,0.001797894,0.003237748,0.000006673713,0.01786554,0.2941251,0.002679326,0.2915745,0.3710635,0.004390771],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.0008849748,0.00001617396,0.7618687,0.00004758757,0.0001194377,0.0005521844,0.001125825,0.0001350255,0.2352501],"genre_scores_gemma":[0.09994654,0.0000105778,0.04356376,0.00004396985,0.00009867253,0.00003770192,0.002423869,0.00007681073,0.8537981],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7183049,"threshold_uncertainty_score":0.9998978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07725340599650285,"score_gpt":0.3232832452640019,"score_spread":0.2460298392674991,"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."}}