{"id":"W4324122516","doi":"10.23977/acss.2023.070203","title":"Lightweight Steel Bar Detection Network Based on YOLOv5","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pyramid (geometry); Feature (linguistics); Bar (unit); Volume (thermodynamics); Computer science; Artificial intelligence; Function (biology); Set (abstract data type); Image (mathematics); Pattern recognition (psychology); Data set; Computer vision; Layer (electronics); Mathematics; Materials science; Geology; Geometry; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005453504,0.0001787169,0.0002993295,0.0002086105,0.000098968,0.00009412396,0.00007425044,0.0001421559,0.000005036567],"category_scores_gemma":[0.000006020152,0.0001546386,0.00004946602,0.0005274003,0.00001198001,0.0001794903,0.00001695779,0.0001705709,0.00006824625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004578021,"about_ca_system_score_gemma":0.000005587961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001437,"about_ca_topic_score_gemma":0.00001257408,"domain_scores_codex":[0.9987453,0.0001192077,0.0003835567,0.0002561114,0.0002032426,0.0002925745],"domain_scores_gemma":[0.9994347,0.0002412826,0.00005452256,0.0001784461,0.00002684353,0.00006418338],"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":[0.00001608474,0.000004918502,0.0002871862,0.00009163417,0.000007267037,0.00001372977,0.00003597142,0.9511772,0.0002843123,0.00006326271,0.0006166236,0.0474018],"study_design_scores_gemma":[0.0004277327,0.0001924116,0.0004975219,0.0003467099,0.000003005815,0.000006901548,0.00002078975,0.9528709,0.0004293157,0.00006444937,0.04495168,0.0001885663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2759499,0.00807857,0.6831072,0.00003488467,0.02562391,0.001410179,0.0000162408,0.001755185,0.004023945],"genre_scores_gemma":[0.9981292,0.0001829758,0.00008724001,0.00002578049,0.001437201,0.00006085209,0.000003554297,0.00002669299,0.00004653199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7221793,"threshold_uncertainty_score":0.6305981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01582794469151093,"score_gpt":0.234337020117469,"score_spread":0.218509075425958,"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."}}