{"id":"W2042937152","doi":"10.1002/qre.1114","title":"Artificial neural network application of modeling failure rate for Boeing 737 tires","year":2010,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"King Fahd University of Petroleum and Minerals","keywords":"Weibull distribution; Artificial neural network; Failure rate; Reliability (semiconductor); Engineering; Computer science; Reliability engineering; Artificial intelligence; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007926864,0.0006688716,0.0004058238,0.0006514514,0.0001933623,0.0003560474,0.0004546928,0.000689377,0.0009662276],"category_scores_gemma":[0.002206845,0.0001978212,0.0003450395,0.0005607918,0.0001706948,0.0003859212,0.0001906062,0.0003736847,0.0002044461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006247379,"about_ca_system_score_gemma":0.0003313383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01514796,"about_ca_topic_score_gemma":0.007975041,"domain_scores_codex":[0.9997804,0.0000768434,0.00001404419,0.00003386949,0.00007252436,0.00002226144],"domain_scores_gemma":[0.999297,0.0003974088,0.00006549962,0.00003498134,0.0001941486,0.00001084745],"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":[0.00004584491,0.00002479876,0.001729728,0.00002062541,0.00002404596,0.00003319311,0.00001471117,0.9779017,0.001058557,0.0002400551,0.0001552656,0.01875149],"study_design_scores_gemma":[9.383953e-7,0.000009281215,0.0003293966,0.000001550758,0.000002604238,0.000003596506,0.000001869585,0.9991665,0.0003405279,0.00009162309,0.0000503651,0.000001834654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6124294,0.000664791,0.3806442,0.0002286186,0.00007088562,0.00006189438,0.0002755481,0.0009289644,0.004695777],"genre_scores_gemma":[0.9808648,0.000151677,0.01739028,0.00001176157,0.00001098769,0.00003924927,0.0001258663,0.00001371781,0.001391598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01514796,"threshold_uncertainty_score":0.03011954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01135295607308184,"score_gpt":0.2493620723061158,"score_spread":0.238009116233034,"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."}}