{"id":"W4318480157","doi":"10.1063/9.0000451","title":"Classification and characterization of coexisting defects from magnetic flux leakage data using deep learning method","year":2023,"lang":"en","type":"article","venue":"AIP Advances","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Magnetic flux leakage; Deep learning; Convolutional neural network; Nondestructive testing; Ferromagnetism; Artificial intelligence; Materials science; Finite element method; Computer science; Characterization (materials science); Leakage (economics); Artificial neural network; Pipeline transport; Machine learning; Structural engineering; Engineering; Mechanical engineering; Magnet; Condensed matter physics; Physics; Nanotechnology","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":[0.0002428554,0.0001066279,0.0001489034,0.00009891774,0.00006656763,0.00002430803,0.0001536782,0.00004391402,0.000007413259],"category_scores_gemma":[0.0002986193,0.0001205965,0.00001017501,0.0003106805,0.00003984735,0.0004298217,0.00009741716,0.0001154828,0.00000335833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002507736,"about_ca_system_score_gemma":0.000005432725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002697891,"about_ca_topic_score_gemma":0.000008201102,"domain_scores_codex":[0.9992797,0.00006553857,0.0001903572,0.0002307639,0.0000974346,0.000136242],"domain_scores_gemma":[0.9992828,0.0003182442,0.00009456241,0.0002445132,0.00003472672,0.00002513849],"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":[0.000002103141,0.000002229763,0.03079573,0.00008380342,0.000005938816,0.000001495321,0.0001353997,0.0003270084,0.9323269,0.0001846741,0.000001412206,0.03613332],"study_design_scores_gemma":[0.0001403438,0.0000442799,0.3148372,0.0002116728,0.00004458132,0.000005685779,0.0001755266,0.6505191,0.02212414,0.01133313,0.0003167827,0.0002475212],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8036059,0.0004071644,0.1947712,0.000004669128,0.00007439373,0.00009548456,0.00002083923,0.0007111054,0.0003092518],"genre_scores_gemma":[0.6292338,0.000152026,0.3702696,0.00000251033,0.00003634969,0.00000491025,0.0002719764,0.00002618984,0.000002578822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9102027,"threshold_uncertainty_score":0.4917782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05744575270271777,"score_gpt":0.3231673884693367,"score_spread":0.2657216357666189,"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."}}