{"id":"W2589626016","doi":"10.1002/qre.2142","title":"Pattern‐based prognostic methodology for condition‐based maintenance using selected and weighted survival curves","year":2017,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prognostics; Survival analysis; Estimator; Reliability (semiconductor); Set (abstract data type); Covariate; Statistics; Computer science; Data mining; Reliability engineering; Mathematics; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.001187848,0.0002280173,0.0003395926,0.00008939685,0.0001962155,0.0001134921,0.0002200569,0.0001580238,0.00002747042],"category_scores_gemma":[0.003025827,0.0002259195,0.00007364399,0.00006460083,0.000162835,0.0002612103,0.00004175649,0.0002022866,6.765583e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077807,"about_ca_system_score_gemma":0.00004357138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007463105,"about_ca_topic_score_gemma":0.00001534816,"domain_scores_codex":[0.9986314,0.00008952628,0.0004404566,0.0003730169,0.0001846024,0.0002810196],"domain_scores_gemma":[0.9982032,0.0008460594,0.0001250028,0.0003457061,0.000383421,0.00009668217],"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":[0.0002543859,0.0002652913,0.07807782,0.0079924,0.0003035221,0.000006266627,0.0002032753,0.8922652,0.009338842,0.006258763,0.0006001199,0.004434122],"study_design_scores_gemma":[0.00089823,0.00003728049,0.09326354,0.0003299012,0.00003399832,0.000003521748,0.000009366783,0.9029189,0.001008065,0.0006676223,0.0005585051,0.0002711017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2406549,0.00009527981,0.7558358,0.001631594,0.0008988,0.000460891,0.0001684804,0.0002009739,0.00005333609],"genre_scores_gemma":[0.9365681,0.0001117521,0.06276439,0.0001295037,0.0001038715,0.00009935952,0.0001685755,0.0000332865,0.00002115353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6959132,"threshold_uncertainty_score":0.9212729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05793070159335487,"score_gpt":0.3303299445134436,"score_spread":0.2723992429200887,"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."}}