{"id":"W1973783189","doi":"10.1007/s10921-014-0260-x","title":"Correlation Between AC Core Loss and Surface Magnetic Barkhausen Noise in Electric Motor Steel","year":2014,"lang":"en","type":"article","venue":"Journal of Nondestructive Evaluation","topic":"Magnetic Properties and Applications","field":"Materials Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Royal Military College of Canada; McGill University; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Barkhausen effect; Materials science; Electrical steel; Eddy current; Magnetization; Grain size; Barkhausen stability criterion; Noise (video); Texture (cosmology); Nuclear magnetic resonance; Acoustics; Condensed matter physics; Composite material; Magnetic field; Electrical engineering; Engineering; 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":[0.0004027633,0.000209129,0.0002050722,0.0009351978,0.0001152376,0.0003007015,0.0001981984,0.0002596759,0.0006850747],"category_scores_gemma":[0.001892174,0.0001640149,0.00007450996,0.000432982,0.0003373387,0.0002549481,0.0001407139,0.0001164561,0.0002190188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001663202,"about_ca_system_score_gemma":0.00006556041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000943195,"about_ca_topic_score_gemma":0.00161302,"domain_scores_codex":[0.999558,0.00005586457,0.00002456179,0.00008280479,0.0002374655,0.00004122735],"domain_scores_gemma":[0.9978797,0.000691297,0.0004790964,0.0001267223,0.0007667418,0.00005640693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009049674,0.00009731855,0.1946092,0.0001204321,0.00007177339,0.0003188801,0.0002654758,0.004846881,0.7701014,0.0001368942,0.0001945155,0.02833215],"study_design_scores_gemma":[0.000007202294,0.0004655276,0.784102,0.000008883948,0.00003250301,0.0004796642,0.0001671868,0.01577632,0.1983215,0.0001105582,0.0005072277,0.00002144621],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972008,0.0001156954,0.002258997,0.000004905833,0.00000226797,0.000003759575,0.00002798654,0.00003284375,0.0003527867],"genre_scores_gemma":[0.9992607,0.00002151882,0.0004014117,0.000003147822,0.000001386361,0.000001437095,0.00004093642,0.000005705864,0.0002637309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000943195,"threshold_uncertainty_score":0.002291799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02871436485645059,"score_gpt":0.2852490773115552,"score_spread":0.2565347124551047,"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."}}