{"id":"W2969971622","doi":"10.1016/j.jallcom.2019.151998","title":"Optimized composition and improved magnetic properties of Ce-Fe-B alloys","year":2019,"lang":"en","type":"article","venue":"Journal of Alloys and Compounds","topic":"Magnetic Properties of Alloys","field":"Materials Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Light Source (Canada); University of Saskatchewan","funders":"Jiangxi University of Science and Technology; Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Alloy; Materials science; Remanence; Microstructure; Tetragonal crystal system; Curie temperature; Analytical Chemistry (journal); Valence (chemistry); Magnetization; Nuclear magnetic resonance; Condensed matter physics; Metallurgy; Phase (matter); Ferromagnetism; Chemistry; Magnetic field","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":[0.0001630679,0.0002657834,0.0003037812,0.0003473587,0.0003633226,0.0004454715,0.000409976,0.0004464094,0.001258423],"category_scores_gemma":[0.0002521363,0.0001827677,0.0001377831,0.000252683,0.0001505284,0.000235842,0.0002288075,0.0002603636,0.000471677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000338257,"about_ca_system_score_gemma":0.000370169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001397497,"about_ca_topic_score_gemma":0.003139302,"domain_scores_codex":[0.9998902,0.00001145813,0.000009464082,0.00002072327,0.000043839,0.00002430412],"domain_scores_gemma":[0.9999334,0.000005923987,0.00001109859,0.000005762675,0.00003406257,0.000009757767],"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":[0.0002141844,0.00003454622,0.0002213516,0.00008023762,0.000006209166,0.00005064274,0.0000166244,0.0003866328,0.9959228,0.0005910432,0.0001788213,0.002296876],"study_design_scores_gemma":[0.00005506115,0.0000852442,0.001237739,0.000008808356,0.00001847657,0.00009495975,0.00002473137,0.002597955,0.9909252,0.0001197084,0.004823726,0.000008485451],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847186,0.001469275,0.005719747,0.0002175124,0.0001028981,0.00004126434,0.0002471619,0.0001429823,0.007340481],"genre_scores_gemma":[0.9835736,0.0003756418,0.01175237,0.00005433777,0.00001596609,0.00002809763,0.0002497141,0.0001089512,0.003841416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001397497,"threshold_uncertainty_score":0.004209876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01041069053827334,"score_gpt":0.1998411264452672,"score_spread":0.1894304359069938,"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."}}