{"id":"W4239507838","doi":"10.1109/tr.2019.2934379","title":"IEEE Transactions on Reliability publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Singapore Management University; Wuhan University; Universidade de São Paulo; Xidian University; Sichuan University; Universidad de Murcia; TU Graz, Internationale Beziehungen und Mobilitätsprogramme; University of Hong Kong; Northwestern Polytechnical University; Centre National de la Recherche Scientifique; Chinese Academy of Sciences; National University of Singapore; University of Alberta; Northwestern University; Arizona State University; Southern Methodist University","keywords":"Reliability (semiconductor); Reliability engineering; Reliability theory; Computer science; Engineering; Failure rate","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008334115,0.0004384558,0.0004109764,0.0003327292,0.0002620819,0.0001399581,0.0003180288,0.0004299273,0.001020398],"category_scores_gemma":[0.00004650175,0.0004375832,0.0003402088,0.0008372479,0.0001355874,0.001945312,4.128667e-7,0.0009123009,0.001565657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008155626,"about_ca_system_score_gemma":0.00009358521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008900026,"about_ca_topic_score_gemma":0.00002438206,"domain_scores_codex":[0.9972515,0.0001416853,0.0009348498,0.0005844957,0.0005583959,0.0005291027],"domain_scores_gemma":[0.9973692,0.0003015261,0.0001178934,0.001494077,0.0004991168,0.0002181642],"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":[0.0001451631,0.0003465068,0.00002421422,0.0002217949,0.00002827709,1.505354e-7,0.0002870808,0.9645551,0.0003691991,0.00005347811,0.0006394702,0.03332957],"study_design_scores_gemma":[0.002030691,0.0006779298,0.00179984,0.000146057,0.0001164573,0.000008858231,0.0002423641,0.9087539,0.07006139,0.001019513,0.01396595,0.001177086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.104372,0.000005491087,0.8814332,0.0007372451,0.002885293,0.001526661,0.000180409,0.001145385,0.00771434],"genre_scores_gemma":[0.9962373,0.0001382614,0.002227297,0.0003380316,0.00003066836,0.0003465881,0.00003616786,0.00004264021,0.0006030927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8918653,"threshold_uncertainty_score":0.9998928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005725311708535436,"score_gpt":0.1968299807793905,"score_spread":0.191104669070855,"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."}}