{"id":"W2124330816","doi":"10.5539/mer.v3n1p99","title":"Probabilistic Design with Gerber Fatigue Model","year":2013,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Probabilistic design; Reliability engineering; Probabilistic logic; Reliability (semiconductor); Sizing; Product design; Product (mathematics); Component (thermodynamics); Optimal design; Quality (philosophy); Computer science; Reduction (mathematics); Engineering; Engineering design process; Mathematics; Power (physics); Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.002971408,0.001474439,0.001501086,0.001723276,0.0005092905,0.001100224,0.001949697,0.001659193,0.004503922],"category_scores_gemma":[0.00472261,0.001149791,0.002512478,0.0007455837,0.0009749625,0.001150215,0.001437683,0.0014341,0.001015351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016953,"about_ca_system_score_gemma":0.000978648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002678205,"about_ca_topic_score_gemma":0.002031761,"domain_scores_codex":[0.997107,0.001059292,0.0001232837,0.0003991498,0.001135209,0.0001761849],"domain_scores_gemma":[0.9980481,0.001103643,0.0002862957,0.0001623354,0.0003653525,0.00003435635],"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":[0.00001961253,0.00001036415,0.0001434215,0.00004477902,0.00002239994,0.00003756823,0.00003095724,0.9748586,0.0009119091,0.01482332,0.0001658021,0.008931274],"study_design_scores_gemma":[0.000006766788,0.00003875974,0.00007586503,0.00001174753,0.00001528795,0.00003405546,0.000003591583,0.9911109,0.0003311569,0.007188554,0.001173677,0.000009600418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002513636,0.0001688357,0.9939471,0.0000550695,0.00001437724,0.00005219363,0.00003539062,0.0001633689,0.003049904],"genre_scores_gemma":[0.5420014,0.001120627,0.4439035,0.0002376209,0.00009883316,0.001019518,0.0002958173,0.0002605164,0.01106219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004503922,"threshold_uncertainty_score":0.01571453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3513993420575405,"score_gpt":0.4022913991173712,"score_spread":0.05089205705983074,"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."}}