{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004782375,0.000244128,0.0002179342,0.0003889206,0.0003962409,0.001208549,0.0003000714,0.00007832319,0.000002976795],"category_scores_gemma":[0.0001562265,0.000213966,0.0001174577,0.0008018244,0.00005251825,0.00114488,0.00005413168,0.000226804,0.00001899323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001951376,"about_ca_system_score_gemma":0.0004203935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001418942,"about_ca_topic_score_gemma":0.000003424314,"domain_scores_codex":[0.9981416,0.00007607743,0.0002962941,0.0005890615,0.0005822036,0.0003147165],"domain_scores_gemma":[0.9991646,0.00009837639,0.0001451759,0.0002259617,0.0002884672,0.00007735018],"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":[0.00006384911,0.000113644,0.0000626625,0.00008552217,0.00001412324,0.00002105741,0.00003862282,0.001058861,0.002245878,0.0001178913,0.00003237253,0.9961455],"study_design_scores_gemma":[0.0007142716,0.0004539412,0.0002863708,0.0006746568,0.00003206684,0.00009723233,0.00004361064,0.8493648,0.1449991,0.002019944,0.000924292,0.0003897096],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004134923,0.0002333373,0.9805861,0.0002386058,0.000246166,0.0001365245,0.000002901847,0.0003125808,0.01410884],"genre_scores_gemma":[0.9939688,2.440831e-7,0.00526671,0.0004829193,0.00006875123,0.00002204032,0.000002190893,0.00001253626,0.0001757496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9957558,"threshold_uncertainty_score":0.9998283,"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."}}