{"id":"W4402439054","doi":"10.11159/icmie24.115","title":"A Novel Approach Integrating Deep Learning and Machine Vision for Tool Wear Detection","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Science and Technology Council","keywords":"Computer science; Artificial intelligence; Machine vision; Deep learning; Machine learning; Computer vision","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":[0.0004851822,0.0009508133,0.001160388,0.001777028,0.0003264956,0.0009246141,0.001720899,0.001164228,0.001672913],"category_scores_gemma":[0.0006685925,0.0004640916,0.0008890704,0.00119685,0.0003016884,0.00105862,0.001361953,0.0009617946,0.0009985813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004838655,"about_ca_system_score_gemma":0.001052034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004174641,"about_ca_topic_score_gemma":0.009640365,"domain_scores_codex":[0.9994903,0.00003642822,0.00001928125,0.0001230865,0.0002336123,0.00009721521],"domain_scores_gemma":[0.9994774,0.00008024502,0.00004445465,0.00009127279,0.0002665733,0.00004006835],"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":[0.0001709233,0.00039873,0.002554363,0.0001285061,0.0001558498,0.0001508967,0.00004933898,0.02924677,0.07867041,0.002274027,0.006215493,0.8799847],"study_design_scores_gemma":[0.00001309212,0.00008735038,0.001399046,0.00001182908,0.00004266124,0.0001697239,0.00001867869,0.9752525,0.01805313,0.002093645,0.002841713,0.00001668197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02503524,0.0006665675,0.9690186,0.0002229562,0.0001985093,0.00008683342,0.0002193886,0.002138365,0.002413478],"genre_scores_gemma":[0.4085831,0.0005368084,0.5772895,0.0005128135,0.0002044288,0.0001117884,0.0007438464,0.0001703306,0.01184742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004174641,"threshold_uncertainty_score":0.008300722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007798180845319869,"score_gpt":0.2110896392303247,"score_spread":0.2032914583850048,"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."}}