{"id":"W4385992970","doi":"10.1515/corrrev-2023-0027","title":"Image recognition model of pipeline magnetic flux leakage detection based on deep learning","year":2023,"lang":"en","type":"article","venue":"Corrosion Reviews","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baker Hughes (Canada)","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Magnetic flux leakage; Artificial intelligence; Pipeline (software); Computer science; Deep learning; Pattern recognition (psychology); Object detection; Cognitive neuroscience of visual object recognition; Leakage (economics); Computer vision; Feature extraction; Engineering","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":[0.0005891624,0.0001891563,0.0003058523,0.0002403198,0.00006104919,0.00001488458,0.0001154232,0.0000841955,0.0001012213],"category_scores_gemma":[0.0006327124,0.0001839216,0.0001082076,0.0005327377,0.00003040305,0.00008811145,0.00002924737,0.0002824547,0.0004142901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006575348,"about_ca_system_score_gemma":0.000006560704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000274918,"about_ca_topic_score_gemma":0.000003784525,"domain_scores_codex":[0.9988934,0.0001132717,0.0004326606,0.0002162501,0.0001532459,0.0001911805],"domain_scores_gemma":[0.9993647,0.0001149301,0.0001125276,0.0002775172,0.00008063877,0.00004974066],"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":[0.000009139533,0.00001201267,0.00005043415,0.000412808,3.617153e-7,0.000001564262,0.00002448625,0.004617143,0.8081743,0.000005577965,0.0006731111,0.1860191],"study_design_scores_gemma":[0.0001640588,0.000150282,0.0001647687,0.0006393386,0.00002236802,0.000002014026,0.000005655068,0.9229843,0.07281858,0.002411273,0.0004426986,0.0001946285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06000237,0.0009449248,0.9283576,0.00001537036,0.0002414048,0.0008781666,0.00001075115,0.002674791,0.006874606],"genre_scores_gemma":[0.6263133,0.003517793,0.3691833,0.0000792705,0.00008771657,0.0003128343,0.0001552311,0.000163333,0.0001871543],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9183672,"threshold_uncertainty_score":0.7500104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03888941616402689,"score_gpt":0.2638529719466322,"score_spread":0.2249635557826053,"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."}}