{"id":"W2149335246","doi":"10.1109/tr.2008.916888","title":"Identifying Optimal Components in a Reliability System","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Generalitat de Catalunya; European Regional Development Fund; Royal Canadian Geographical Society","keywords":"Reliability (semiconductor); Component (thermodynamics); Reliability theory; Reliability engineering; Measure (data warehouse); Computer science; Value (mathematics); Process (computing); Failure rate; Data mining; Engineering; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001399662,0.001231434,0.001197891,0.002429889,0.0008699943,0.001360085,0.0006903879,0.001131685,0.002379178],"category_scores_gemma":[0.004753002,0.000987991,0.0005513968,0.001168538,0.001128846,0.00128263,0.001021078,0.0008488792,0.0005016685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001272446,"about_ca_system_score_gemma":0.002237248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004347467,"about_ca_topic_score_gemma":0.002941443,"domain_scores_codex":[0.998964,0.0003537455,0.00006221059,0.0002140402,0.0002725079,0.0001336759],"domain_scores_gemma":[0.9991069,0.0004548301,0.000116072,0.00005549466,0.0002231696,0.00004343058],"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":[0.0002266434,0.00008560494,0.003137152,0.000316531,0.00007265307,0.0001773523,0.0003681594,0.8082607,0.01477953,0.06806366,0.00146278,0.1030493],"study_design_scores_gemma":[0.00003826039,0.0001737534,0.001274896,0.0000557794,0.00007014851,0.00007220263,0.000167523,0.9382631,0.004833372,0.05191766,0.003097017,0.00003630732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08069288,0.0003308989,0.914791,0.0002090214,0.00001587866,0.0001407251,0.00008590075,0.0003570794,0.003376536],"genre_scores_gemma":[0.5240478,0.0004559397,0.4726332,0.00004895243,0.00002058396,0.0003558508,0.0001622311,0.0001642022,0.002111345],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004347467,"threshold_uncertainty_score":0.009232223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02361647586340717,"score_gpt":0.2187626025675685,"score_spread":0.1951461267041614,"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."}}