{"id":"W2746268652","doi":"10.1109/tmag.2017.2662712","title":"The Modified Jiles–Atherton Model for the Accurate Prediction of Iron Losses","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Magnetic Properties and Applications","field":"Materials Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Ferromagnetism; Hysteresis; Loop (graph theory); Magnetic hysteresis; Magnetic field; Magnetic flux; Magnetic domain; Finite element method; Intensity (physics); Materials science; Flux (metallurgy); Domain (mathematical analysis); Computational physics; Condensed matter physics; Computer science; Nuclear magnetic resonance; Physics; Thermodynamics; Mathematical analysis; Optics; Magnetization; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006565436,0.000723288,0.00074139,0.0007965177,0.0003741951,0.0005818193,0.002151527,0.001752966,0.0017806],"category_scores_gemma":[0.00162503,0.0004744294,0.0007707826,0.0005012837,0.0005270467,0.001045765,0.0004947178,0.0009473419,0.0007452911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006195696,"about_ca_system_score_gemma":0.0005888843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005325386,"about_ca_topic_score_gemma":0.004745105,"domain_scores_codex":[0.9996079,0.00009327272,0.00001522701,0.00004862833,0.0001954792,0.0000395811],"domain_scores_gemma":[0.9995626,0.0002172347,0.00004610995,0.00005713079,0.00009755269,0.00001940407],"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":[0.00007921801,0.0000465001,0.0005462098,0.0001037786,0.00003533636,0.0002541982,0.0001015644,0.9393209,0.01117891,0.02224763,0.002045573,0.02404018],"study_design_scores_gemma":[0.000002546146,0.000008985192,0.000103069,0.000005130869,0.000002583124,0.00001647188,0.000002276679,0.9968535,0.0003877755,0.001502683,0.0011099,0.00000512161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03102646,0.002182325,0.9516172,0.0003955557,0.0001942158,0.0000743598,0.0001642347,0.0006678301,0.01367783],"genre_scores_gemma":[0.8004375,0.002472833,0.1593619,0.000312391,0.0001748496,0.0004547757,0.0003324571,0.0004451739,0.03600812],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005325386,"threshold_uncertainty_score":0.01058877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05927582267438907,"score_gpt":0.2779079439969646,"score_spread":0.2186321213225755,"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."}}