{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006183719,0.0003059519,0.0004178863,0.0002048767,0.0002266582,0.00002652642,0.0002345206,0.0002325104,0.0000501421],"category_scores_gemma":[0.00002919523,0.0003165741,0.0002071093,0.0005810805,0.0001964107,0.0004027602,0.000001727929,0.0006177982,0.00009376241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009348787,"about_ca_system_score_gemma":0.00004219435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002373768,"about_ca_topic_score_gemma":0.00003707631,"domain_scores_codex":[0.9977337,0.00014904,0.000729288,0.0005639516,0.0003541406,0.000469843],"domain_scores_gemma":[0.9987081,0.0001668582,0.00004860301,0.0008139111,0.0001170538,0.0001454224],"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":[0.00008427999,0.0002790109,0.0005784706,0.0003136096,0.00001215687,0.0000139887,0.0004348821,0.9967165,0.0006891296,0.00002064707,0.00006253186,0.0007948067],"study_design_scores_gemma":[0.001368481,0.0001059537,0.02363539,0.0002549114,0.00003130146,0.00005889908,0.0003048296,0.9615798,0.01168364,0.0001108041,0.0002598695,0.0006061067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5697891,0.00001819493,0.4272315,0.00004084275,0.0008757699,0.0004594296,0.0000239444,0.000573589,0.0009876288],"genre_scores_gemma":[0.9931577,0.0001425397,0.006376357,0.00001834294,0.00002462207,0.0001657807,0.000006605072,0.00003548714,0.00007257452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4233686,"threshold_uncertainty_score":0.9999287,"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."}}