{"id":"W2792162769","doi":"10.1016/j.apm.2018.02.020","title":"Reliability modelling and assessment of a heterogeneously repaired system with partially relevant recurrence data","year":2018,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Estimator; Reliability (semiconductor); Reliability engineering; Computer science; Bayesian probability; Importance sampling; Preventive maintenance; Process (computing); Bayesian inference; Statistical model; Sampling (signal processing); Data mining; Algorithm; Statistics; Engineering; Machine learning; Monte Carlo method; Artificial intelligence; 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":[0.0007742196,0.0002023649,0.0003973993,0.00003809686,0.00007820441,0.00003572045,0.0002444665,0.00009870699,0.000005007951],"category_scores_gemma":[0.00002085769,0.0001623227,0.00002670861,0.0001367055,0.000188773,0.0001536603,0.0001035568,0.0001644055,0.000004419351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006071396,"about_ca_system_score_gemma":0.00003965558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000830212,"about_ca_topic_score_gemma":0.000003231223,"domain_scores_codex":[0.9983742,0.00002379419,0.0005824517,0.0004710771,0.0002720553,0.0002764242],"domain_scores_gemma":[0.9985263,0.0001578513,0.00009496303,0.0009985882,0.0001256328,0.00009662902],"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":[0.00003004911,0.0000400827,0.000004164566,0.001100169,0.00002586248,0.00000105889,0.0002341605,0.9629967,0.0001952452,0.0349319,0.000006233411,0.0004343488],"study_design_scores_gemma":[0.0001970018,0.00006321215,0.000001070136,0.0004009476,0.00005150617,0.00001027643,0.00008961802,0.9892588,0.0006218642,0.009090496,0.00003174875,0.00018348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07776735,0.00004017818,0.9183049,0.00001172303,0.00003655294,0.0004874551,0.00001154663,0.0002400615,0.003100249],"genre_scores_gemma":[0.593959,0.00005831818,0.4058946,0.000002936361,0.00002282972,0.00002846003,0.000008943831,0.00002277779,0.000002082386],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5161917,"threshold_uncertainty_score":0.6619326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03804899703789025,"score_gpt":0.2504365910240899,"score_spread":0.2123875939861996,"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."}}