{"id":"W4415438333","doi":"10.1007/978-3-031-97435-9_13","title":"Application of Machine Learning (ML) for the Prediction of Stress Concentration and Fatigue Life","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"SAIT Polytechnic","funders":"","keywords":"Bridge (graph theory); Stress (linguistics); Stress concentration; Connection (principal bundle); Bearing (navigation); Service life","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":[0.0007445872,0.0007311065,0.0007246413,0.0007751251,0.0001944364,0.0006945977,0.0006631921,0.0007723744,0.002303426],"category_scores_gemma":[0.002336236,0.0003357532,0.0005837666,0.001080408,0.0002550779,0.000722866,0.0005404197,0.001081538,0.0010358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003162349,"about_ca_system_score_gemma":0.0002815086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001975533,"about_ca_topic_score_gemma":0.002031438,"domain_scores_codex":[0.9997306,0.00007111151,0.00002080003,0.00006845251,0.00009269064,0.00001619624],"domain_scores_gemma":[0.9985379,0.001132747,0.00007196787,0.00008402261,0.0001573211,0.00001597275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000107853,0.00009878026,0.002219407,0.0001751848,0.00009923007,0.00006183922,0.00004294305,0.3476308,0.009985764,0.002899576,0.003426505,0.6332521],"study_design_scores_gemma":[0.00000306354,0.0000322795,0.0007242136,0.000007264728,0.00001039033,0.00002591049,0.000003635453,0.992816,0.002878351,0.002507365,0.0009838246,0.000007816648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02466941,0.003004452,0.9649294,0.0003546675,0.0002369561,0.00002850773,0.000244324,0.002199133,0.004333191],"genre_scores_gemma":[0.6274843,0.00253909,0.3567052,0.000256083,0.0004985582,0.0001342863,0.0006451551,0.0002729611,0.01146436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002303426,"threshold_uncertainty_score":0.007705688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006735269567157133,"score_gpt":0.1972358511750854,"score_spread":0.1905005816079283,"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."}}