{"id":"W4392002532","doi":"10.3389/fmtec.2024.1277152","title":"Anomaly detection in automated fibre placement: learning with data limitations","year":2024,"lang":"en","type":"article","venue":"Frontiers in Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Anomaly (physics); Computer science; Artificial intelligence; Pattern recognition (psychology); Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001179916,0.0007228702,0.0006156675,0.0006031371,0.0003013455,0.0005573918,0.001240651,0.001070546,0.0007108936],"category_scores_gemma":[0.004099106,0.0004407254,0.0004589919,0.0004940865,0.0008123655,0.000929846,0.00094448,0.001076514,0.0003377139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006449942,"about_ca_system_score_gemma":0.0007516018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004381317,"about_ca_topic_score_gemma":0.005345636,"domain_scores_codex":[0.9993831,0.00009211911,0.00002585542,0.0002259527,0.0002014389,0.00007149614],"domain_scores_gemma":[0.99794,0.001046893,0.0002580264,0.0003002699,0.0003854829,0.00006938642],"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":[0.000299981,0.0001446902,0.010548,0.0001860379,0.00008450876,0.000353757,0.0001777088,0.5330339,0.03863736,0.002156118,0.002625914,0.4117521],"study_design_scores_gemma":[0.000005509572,0.00005849065,0.001977639,0.00001193791,0.000008999624,0.0001196097,0.00002491297,0.9839281,0.01079445,0.002379461,0.0006826039,0.000008230827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1680419,0.0003836136,0.8280159,0.0003101228,0.00004462122,0.00004683293,0.0001665265,0.002138085,0.0008523179],"genre_scores_gemma":[0.8081375,0.0002120188,0.189361,0.000137561,0.00003992517,0.0000648037,0.0005617797,0.0001010872,0.001384368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004381317,"threshold_uncertainty_score":0.008711636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01908094156064225,"score_gpt":0.2286261055192868,"score_spread":0.2095451639586446,"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."}}