{"id":"W2809944199","doi":"10.1007/s42243-018-0103-6","title":"Multi-class classification method for strip steel surface defects based on support vector machine with adjustable hyper-sphere","year":2018,"lang":"en","type":"article","venue":"Journal of Iron and Steel Research International","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Support vector machine; Pattern recognition (psychology); Binary classification; Mathematics; One-class classification; Surface (topology); Artificial intelligence; Feature vector; Classifier (UML); Structured support vector machine; Minification; Multiclass classification; Computer science; Algorithm; Mathematical optimization; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002212079,0.0001390092,0.0002202365,0.0002701527,0.0001467591,0.000137832,0.0001812615,0.0001315515,0.0001623973],"category_scores_gemma":[0.0003061993,0.0001032499,0.00007805906,0.0001940269,0.00005532437,0.0002033069,0.0000181802,0.0004711296,0.00001286351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002742051,"about_ca_system_score_gemma":0.0001524221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000254133,"about_ca_topic_score_gemma":0.00005977221,"domain_scores_codex":[0.998145,0.000170156,0.0003897614,0.0001772136,0.0008526692,0.0002651594],"domain_scores_gemma":[0.9980984,0.0004576801,0.0001408613,0.0001382019,0.001000301,0.0001644988],"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":[0.01419351,0.0012874,0.008159707,0.0007196466,0.0007094151,0.00009541335,0.0009429474,0.1190562,0.6327148,0.002461855,0.0802874,0.1393717],"study_design_scores_gemma":[0.004501219,0.002902771,0.007375861,0.0001744695,0.00001825051,0.00006253691,0.0003849894,0.8704112,0.01112965,0.00001511218,0.1028542,0.0001698066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6591235,0.000252094,0.3235074,0.0009615549,0.003339565,0.001291927,0.0002132825,0.00009424002,0.01121649],"genre_scores_gemma":[0.9895411,0.00002510254,0.008147622,0.00003231362,0.000636973,0.00001187411,0.00001045617,0.0000320802,0.001562526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7513549,"threshold_uncertainty_score":0.4210407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09428210083464171,"score_gpt":0.3749240949677056,"score_spread":0.2806419941330639,"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."}}