{"id":"W2123258030","doi":"10.1109/tmag.2013.2283659","title":"Kinetic Monte Carlo Simulations of $M\\!\\!-\\!\\!H$ Loops for HAMR Recording Media: Comparison With MOKE Data","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Magnetic properties of thin films","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Western Digital","keywords":"Monte Carlo method; Anisotropy; Condensed matter physics; Heat-assisted magnetic recording; Micromagnetics; Saturation (graph theory); Computational physics; Patterned media; Physics; Kinetic energy; Magnetization; Kinetic Monte Carlo; Materials science; Magnetic anisotropy; Kerr effect; Nuclear magnetic resonance; Optics; Magnetic field; Acoustics; Classical mechanics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008844226,0.0002529901,0.0003613367,0.0001188608,0.0001692132,0.00006696664,0.0005559232,0.00006993359,0.001733573],"category_scores_gemma":[0.000008863974,0.0002199223,0.00008569083,0.0001943736,0.0001516725,0.0002155998,0.000008918941,0.0002582739,0.00003137306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001727636,"about_ca_system_score_gemma":0.0000593714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001518761,"about_ca_topic_score_gemma":0.000175601,"domain_scores_codex":[0.9984795,0.00004228025,0.0004888202,0.00039266,0.0002677811,0.0003289949],"domain_scores_gemma":[0.9981132,0.0003290584,0.0001704236,0.001018995,0.0002409874,0.0001273157],"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":[0.0003134969,0.001870112,0.00283683,0.0002366161,0.0004785039,9.508922e-7,0.001660539,0.6604927,0.006701406,0.0004631379,0.01184159,0.3131041],"study_design_scores_gemma":[0.002667604,0.001365688,0.0007462852,0.0001482348,0.0005846915,0.000001591121,0.001021288,0.967865,0.01944949,0.00085943,0.00460237,0.0006883621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1893978,0.0001270067,0.8042401,0.0007349759,0.000772607,0.001581452,0.001441602,0.00008372107,0.001620699],"genre_scores_gemma":[0.971378,0.000005953528,0.02706015,0.00003178307,0.0001024889,0.0001081048,0.00005243347,0.00004884683,0.001212249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7819802,"threshold_uncertainty_score":0.999179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04207870123879296,"score_gpt":0.2634982303931036,"score_spread":0.2214195291543107,"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."}}