{"id":"W1608194804","doi":"10.1063/1.4926474","title":"Accurate mean-field modeling of the Barkhausen noise power in ferromagnetic materials, using a positive-feedback theory of ferromagnetism","year":2015,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Magnetic Properties and Applications","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Barkhausen effect; Ferromagnetism; Condensed matter physics; Hysteresis; Magnetic hysteresis; Materials science; Magnetization; Field (mathematics); Envelope (radar); Demagnetizing field; Barkhausen stability criterion; Statistical physics; Noise (video); Physics; Magnetic field; Mathematics; Computer science; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007282113,0.0001534005,0.0003977758,0.00004605098,0.00004422143,0.0000343518,0.0004991394,0.00006852047,0.0001176524],"category_scores_gemma":[0.00003679585,0.0001036316,0.00008058016,0.0002161098,0.000107412,0.0001384592,0.0001670191,0.0001495564,0.000004689905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004128402,"about_ca_system_score_gemma":0.0001882934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009332346,"about_ca_topic_score_gemma":0.000003812838,"domain_scores_codex":[0.9984146,0.00009253641,0.0007542075,0.0001412379,0.0003955918,0.0002018201],"domain_scores_gemma":[0.9986227,0.00007257341,0.0006528624,0.0003289405,0.0002472782,0.00007566337],"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":[0.0003505629,0.0001280975,0.000005980115,0.00002661565,0.000008303927,0.000001053453,0.001627414,0.08207297,0.909874,0.005656451,0.00003387016,0.000214676],"study_design_scores_gemma":[0.001369227,0.0004057754,0.0003807738,0.0002531967,0.0001050398,0.00002244275,0.001780016,0.04208639,0.9059303,0.0474146,0.00001927283,0.000232904],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945894,0.000144752,0.00238718,0.00007422654,0.0001860066,0.000240082,0.00001770616,0.00000430159,0.002356324],"genre_scores_gemma":[0.997345,0.00001443093,0.002431683,0.00008445121,0.00008396278,0.000004654996,5.4862e-7,0.0000182298,0.00001704483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04175815,"threshold_uncertainty_score":0.4225975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03737047830541576,"score_gpt":0.252607648852228,"score_spread":0.2152371705468123,"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."}}