{"id":"W2336675174","doi":"10.1177/1748006x16631202","title":"Reliability estimation considering usage rate profile and warranty claims","year":2016,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Warranty; Reliability (semiconductor); Reliability engineering; Failure rate; Computer science; Population; Field (mathematics); Estimation; Task (project management); Engineering; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00222339,0.000161921,0.0003885822,0.00006998677,0.00008050494,0.00001426104,0.0001483465,0.0001536441,0.000007249755],"category_scores_gemma":[0.003236501,0.00009133382,0.000124408,0.0001752424,0.0003758279,0.0005199512,0.00005796448,0.0002719881,3.001584e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008555996,"about_ca_system_score_gemma":0.00004338268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005936593,"about_ca_topic_score_gemma":5.896256e-7,"domain_scores_codex":[0.9985811,0.00003074735,0.0008134397,0.0001708062,0.0002405523,0.0001633464],"domain_scores_gemma":[0.9986071,0.0002614339,0.0003901428,0.0001314506,0.0005032732,0.0001066056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001376555,0.0005523866,0.0174927,0.005283331,0.0002716767,0.000001952698,0.002164494,0.6837084,0.2095862,0.03235359,0.001259583,0.04594913],"study_design_scores_gemma":[0.005116991,0.0007978506,0.02854873,0.003599829,0.0005440312,0.0001498115,0.0006167075,0.4496611,0.3983595,0.109917,0.00185301,0.0008354475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9493166,0.0001420188,0.04934712,0.0002630938,0.0005496662,0.0002585135,0.00001787787,0.00003364981,0.00007145357],"genre_scores_gemma":[0.9864689,0.001924476,0.01154614,0.00000465484,0.00003497474,0.000004302344,2.976454e-7,0.00000975651,0.000006524097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2340473,"threshold_uncertainty_score":0.3874628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006109775821410678,"score_gpt":0.1960387168812009,"score_spread":0.1899289410597902,"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."}}