{"id":"W4408059151","doi":"10.1016/j.dsp.2025.105120","title":"A measurement anomaly detection method on metal cartridge cases based on PointNet++","year":2025,"lang":"en","type":"article","venue":"Digital Signal Processing","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture","funders":"","keywords":"Cartridge; Anomaly detection; Anomaly (physics); Computer science; Artificial intelligence; Pattern recognition (psychology); Materials science; Metallurgy; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002911721,0.0008095449,0.0006954541,0.001981746,0.0005844189,0.0006926628,0.001369011,0.0008159645,0.00226554],"category_scores_gemma":[0.0009129007,0.0003179398,0.0004682142,0.001291418,0.0005056069,0.001124792,0.0009433743,0.0004564282,0.0008829722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002899933,"about_ca_system_score_gemma":0.0005613634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003382217,"about_ca_topic_score_gemma":0.002845343,"domain_scores_codex":[0.9994109,0.00004334116,0.00003039235,0.0001610584,0.0002993513,0.00005492643],"domain_scores_gemma":[0.9996883,0.00005334904,0.00003560468,0.00006015604,0.0001394071,0.00002315127],"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":[0.000338682,0.000111247,0.01221476,0.0001620237,0.00006019948,0.0008693749,0.0001199609,0.04849238,0.1005185,0.006178163,0.003692691,0.827242],"study_design_scores_gemma":[0.00001579797,0.0001080262,0.004710856,0.00001312992,0.00003909227,0.00106543,0.00007509856,0.9504728,0.03610082,0.003581312,0.003790174,0.00002748562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08928357,0.0002620436,0.9027402,0.0001378164,0.0001036869,0.0000940504,0.0001726132,0.004522676,0.002683306],"genre_scores_gemma":[0.6235151,0.000257242,0.3721977,0.00006103365,0.00006313367,0.00006168671,0.0005248478,0.0001467381,0.003172501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003382217,"threshold_uncertainty_score":0.007578969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02154424103725922,"score_gpt":0.2635519083643219,"score_spread":0.2420076673270627,"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."}}