{"id":"W2524685625","doi":"10.1063/1.4963756","title":"Hysteresis loops revisited: An efficient method to analyze ferroic materials","year":2016,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Multiferroics and related materials","field":"Materials Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Regroupement Québécois sur les Matériaux de Pointe; Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Hysteresis; Ferroelectricity; Ferromagnetism; Multiferroics; Magnetic hysteresis; Materials science; Condensed matter physics; Reliability (semiconductor); Loop (graph theory); Preisach model of hysteresis; Computer science; Statistical physics; Magnetization; Physics; Optoelectronics; Mathematics; Thermodynamics; Magnetic field","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":[0.0006652729,0.0006605671,0.0004513373,0.002484769,0.0002740705,0.0007960242,0.0009112325,0.0007873643,0.003929339],"category_scores_gemma":[0.001763926,0.0002769687,0.0004174827,0.001378896,0.0004628832,0.001162853,0.0008069829,0.0009327427,0.0009437801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001944149,"about_ca_system_score_gemma":0.0003786278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004371608,"about_ca_topic_score_gemma":0.00048657,"domain_scores_codex":[0.9996263,0.00007827515,0.0000243333,0.00006925884,0.0001672573,0.00003458433],"domain_scores_gemma":[0.9993575,0.000284868,0.00009458243,0.0001228001,0.000112436,0.00002771906],"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":[0.0002843694,0.0001176696,0.002707254,0.0006741412,0.0001398725,0.0005028328,0.0004215084,0.0169072,0.4665678,0.04141285,0.004706659,0.4655578],"study_design_scores_gemma":[0.00006659732,0.0002253932,0.004498991,0.00007659108,0.00009140981,0.001823902,0.0002201044,0.7110226,0.1984939,0.04394569,0.03941215,0.0001227501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01814119,0.0008537257,0.9755608,0.00009693119,0.00005778495,0.00005717081,0.0002865675,0.00285239,0.002093491],"genre_scores_gemma":[0.2367962,0.0008796841,0.7573772,0.0001005023,0.00008129012,0.0001631734,0.0004483309,0.0006090021,0.003544491],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003929339,"threshold_uncertainty_score":0.01314497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02051630703116779,"score_gpt":0.3005294170286205,"score_spread":0.2800131099974527,"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."}}