{"id":"W4220779705","doi":"10.1155/2022/1172654","title":"Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature Fusion","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Education Department of Hunan Province","keywords":"Tread; Train; Feature (linguistics); Computer science; Fuse (electrical); Feature extraction; Fusion; Pattern recognition (psychology); Artificial intelligence; Sensor fusion; Data mining; Engineering; Materials science","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":[0.0003397851,0.0005046639,0.0005711667,0.001084421,0.0002176338,0.0004496606,0.0007044442,0.0004948548,0.001355605],"category_scores_gemma":[0.000632092,0.0002257017,0.0004818098,0.0005004458,0.0001927642,0.0009638632,0.0007666565,0.0003168635,0.0004000552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004006447,"about_ca_system_score_gemma":0.0004169911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003162183,"about_ca_topic_score_gemma":0.003374298,"domain_scores_codex":[0.9997236,0.00002366034,0.00001351651,0.00007436799,0.0001257946,0.00003897777],"domain_scores_gemma":[0.9997754,0.00002750911,0.0000394618,0.00003185584,0.0001077947,0.00001810147],"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":[0.0005654816,0.0001798602,0.01231108,0.0001840151,0.0001278096,0.0003152626,0.0001378009,0.0741475,0.1700728,0.002079765,0.004314557,0.7355639],"study_design_scores_gemma":[0.00002470543,0.0002051079,0.01423961,0.00001323966,0.00007125759,0.0003031813,0.00005835009,0.9365154,0.04532791,0.001018738,0.002184927,0.00003750695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1269665,0.0001763903,0.867925,0.0000730806,0.00004526643,0.00007915496,0.0002154139,0.002781679,0.001737574],"genre_scores_gemma":[0.8168679,0.0001502581,0.1795888,0.00005930977,0.00002565048,0.00008261814,0.0005532411,0.00007952451,0.002592766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003162183,"threshold_uncertainty_score":0.006287515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003225307747779058,"score_gpt":0.1903749290713146,"score_spread":0.1871496213235356,"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."}}