{"id":"W4400956229","doi":"10.1016/j.aei.2024.102717","title":"Defect detection on multi-type rail surfaces via IoU decoupling and multi-information alignment","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Science Basic Research Program of Shaanxi Province; Natural Science Foundation of Hunan Province; China Scholarship Council; University of Waterloo; National Natural Science Foundation of China","keywords":"Decoupling (probability); Computer science; Artificial intelligence; Engineering; Materials science; Control engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003124618,0.000654838,0.0006039923,0.0009387509,0.0002793188,0.0006282661,0.0005506423,0.0005995419,0.00136607],"category_scores_gemma":[0.0008110699,0.0002912631,0.0003282311,0.0008199114,0.000337768,0.0009822812,0.0008897344,0.0004023997,0.0003900786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003287488,"about_ca_system_score_gemma":0.0004772366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000795205,"about_ca_topic_score_gemma":0.001502273,"domain_scores_codex":[0.9995846,0.00004231361,0.00001541923,0.00008282461,0.0002068151,0.00006803234],"domain_scores_gemma":[0.9995375,0.00008764452,0.00008752224,0.0001011365,0.0001562988,0.00002986659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007446289,0.0001745593,0.007211346,0.0001895918,0.00005115961,0.0002417749,0.0002087122,0.01636646,0.6617763,0.005958849,0.001233145,0.3058435],"study_design_scores_gemma":[0.0000315661,0.0004096792,0.01292817,0.000029392,0.0000574773,0.0005577743,0.0001972959,0.6195534,0.3590584,0.003201998,0.003913262,0.00006159831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3184802,0.0003613768,0.67201,0.0002089538,0.00007520779,0.00003973436,0.0001075211,0.001847835,0.006869211],"genre_scores_gemma":[0.8432854,0.00009210404,0.1541529,0.00006467332,0.00001474449,0.00002656698,0.0001294929,0.00008769958,0.002146384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00136607,"threshold_uncertainty_score":0.004570007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01098477162487129,"score_gpt":0.2271057419252353,"score_spread":0.216120970300364,"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."}}