{"id":"W4403941405","doi":"10.1002/pssb.202400298","title":"Applying Machine Learning to Elucidate Ultrafast Demagnetization Dynamics in Ni and Ni<sub>80</sub>Fe<sub>20</sub>","year":2024,"lang":"en","type":"article","venue":"physica status solidi (b)","topic":"Magnetic properties of thin films","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Demagnetizing field; Ultrashort pulse; Dynamics (music); Materials science; Statistical physics; Chemical physics; Computer science; Condensed matter physics; Physics; Optics; Magnetic field; Magnetization; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002296796,0.0004800382,0.0004722431,0.0002270592,0.0002418631,0.0003817591,0.0002638501,0.00007722717,0.00004877269],"category_scores_gemma":[0.0000349059,0.0004888511,0.0001108486,0.0006043385,0.00009652086,0.0003929382,0.0003120086,0.0006910546,0.0001982872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001658798,"about_ca_system_score_gemma":0.0001098887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000684243,"about_ca_topic_score_gemma":0.0002252691,"domain_scores_codex":[0.9973059,0.000124063,0.0004835271,0.000809282,0.0003644349,0.0009127849],"domain_scores_gemma":[0.9990178,0.0001030827,0.0001243162,0.0003860166,0.00007957385,0.0002892071],"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":[0.0001016665,0.0002377932,0.00666634,0.0003166101,0.0001265283,0.00001646709,0.002373705,0.00539585,0.7152339,0.005774889,0.0008939617,0.2628623],"study_design_scores_gemma":[0.001617251,0.0005466825,0.002308875,0.0006864935,0.0002136879,0.00000745616,0.001479215,0.5581375,0.4159011,0.006202774,0.01101207,0.001886805],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888914,0.0005884641,0.004182428,0.0005427991,0.0003942772,0.001006403,0.000207304,0.0002315617,0.003955324],"genre_scores_gemma":[0.9978254,0.00009065946,0.0003813869,0.0001175278,0.0003622971,0.0003545177,0.0004433148,0.0001244449,0.0003004539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5527417,"threshold_uncertainty_score":0.9997563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006725223914148344,"score_gpt":0.2189258170207972,"score_spread":0.2122005931066488,"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."}}