{"id":"W2035508719","doi":"10.1002/nav.20023","title":"A unified model incorporating yield, burn‐in, and reliability","year":2004,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Science Foundation","keywords":"Reliability (semiconductor); Yield (engineering); Reliability engineering; Burn-in; Function (biology); Poisson distribution; Computer science; Mathematics; Applied mathematics; Statistics; Engineering; Physics; Thermodynamics","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":[0.001924763,0.001476572,0.001875095,0.001228144,0.0005539812,0.002256182,0.003826257,0.003167814,0.004921931],"category_scores_gemma":[0.004238893,0.001034636,0.001328823,0.001108251,0.001321484,0.00305511,0.001172775,0.001432161,0.0009585443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001913588,"about_ca_system_score_gemma":0.001695064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008599743,"about_ca_topic_score_gemma":0.004940446,"domain_scores_codex":[0.9991446,0.0002430781,0.00004009502,0.0002093579,0.0002204181,0.0001423642],"domain_scores_gemma":[0.9987082,0.0006026315,0.0002077671,0.0001007332,0.0002901749,0.00009049841],"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":[0.00002378939,0.0000224512,0.0001921561,0.00002787795,0.00001409475,0.0000802084,0.00003248965,0.979746,0.0006716692,0.01667027,0.0003207198,0.002198252],"study_design_scores_gemma":[0.000008700694,0.00002447962,0.0000851712,0.000004833972,0.00001550759,0.00001875654,0.000006354779,0.9956185,0.0001402991,0.003668046,0.0004013283,0.000008104666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.076758,0.0009636859,0.8948141,0.001020083,0.000141692,0.0002147155,0.0009725125,0.0007055979,0.02440958],"genre_scores_gemma":[0.9028077,0.001382351,0.04627081,0.0001660941,0.0001120946,0.0006433877,0.0005846102,0.0001861685,0.04784672],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008599743,"threshold_uncertainty_score":0.01709938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07724833844691953,"score_gpt":0.3232252662938739,"score_spread":0.2459769278469544,"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."}}