{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005988841,0.000198007,0.0005584641,0.0001199339,0.000072959,0.0001497031,0.0002947677,0.00008169506,0.0002928315],"category_scores_gemma":[0.00002715398,0.0001409096,0.00008664503,0.00006542941,0.0002528253,0.0003554065,0.00017062,0.000165556,0.00000862564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002614796,"about_ca_system_score_gemma":0.00008462996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006562905,"about_ca_topic_score_gemma":0.000002731372,"domain_scores_codex":[0.9983793,0.0001067641,0.0007020651,0.0002058424,0.0003594131,0.0002466017],"domain_scores_gemma":[0.9988188,0.00005496495,0.0004758079,0.0002170639,0.0002879898,0.0001453306],"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":[0.0006952481,0.0001054946,0.0002639359,0.0001730565,0.00002483394,0.000005274222,0.0004189208,0.00005129952,0.9972781,0.000148771,0.0001741217,0.0006609309],"study_design_scores_gemma":[0.01444062,0.01090784,0.0104208,0.00148842,0.0003995466,0.001675487,0.001617563,0.01243019,0.9397748,0.001076333,0.004608484,0.001159978],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930298,0.004647767,0.00007871765,0.000587091,0.0003753802,0.0002315464,0.000003391978,0.00001487417,0.001031465],"genre_scores_gemma":[0.9904857,0.0004750475,0.008374797,0.0001481261,0.00008067742,0.000002869985,6.684407e-7,0.0000187877,0.0004133045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05750338,"threshold_uncertainty_score":0.5746127,"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."}}