{"id":"W2748362289","doi":"10.3233/jae-172252","title":"Relevance of microstructure and texture to the accuracy and interpretation of 1 and 2 directional characterisation and testing of grain-oriented electrical steels","year":2017,"lang":"en","type":"article","venue":"International Journal of Applied Electromagnetics and Mechanics","topic":"Magnetic Properties and Applications","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Electrical steel; Eddy current; Harmonics; Microstructure; Anisotropy; Texture (cosmology); Flux (metallurgy); Magnetization; Materials science; Hysteresis; Transverse plane; Field (mathematics); Relevance (law); Condensed matter physics; Metallurgy; Magnetic field; Voltage; Computer science; Structural engineering; Engineering; Electrical engineering; Physics; Mathematics; Optics; Artificial intelligence","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":[0.0002763863,0.00008789516,0.000179886,0.00006332542,0.0001004002,0.00006770773,0.000149844,0.00004611,0.000004926998],"category_scores_gemma":[0.0003369586,0.0000640721,0.000013991,0.00004227809,0.0001011747,0.00008397692,0.0001104226,0.0001038091,4.012596e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009040214,"about_ca_system_score_gemma":0.00003280331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001787459,"about_ca_topic_score_gemma":0.000007172256,"domain_scores_codex":[0.9992097,0.00001544463,0.0003532521,0.0001322613,0.0002051301,0.00008423308],"domain_scores_gemma":[0.9987159,0.000158416,0.0006290774,0.00009262704,0.0003539367,0.00005002586],"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":[0.0002019179,0.00001659054,0.0001044766,0.00002396423,0.00001489681,2.860349e-7,0.0004388111,0.000006715019,0.915095,0.009216083,0.00001297328,0.07486822],"study_design_scores_gemma":[0.001961754,0.002612208,0.05844127,0.0002703668,0.0001757687,0.0005066541,0.0003585171,0.02900927,0.8747935,0.02918953,0.002373702,0.0003075102],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954323,0.0006115811,0.002880543,0.0007612011,0.00007288377,0.0001572019,0.00002859433,0.000002082493,0.00005366814],"genre_scores_gemma":[0.9934925,0.0008153966,0.005559814,0.0000591447,0.00005058246,0.000004311568,0.000001656188,0.000005776313,0.00001082016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07456072,"threshold_uncertainty_score":0.2612784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008203938191429335,"score_gpt":0.2468506560928589,"score_spread":0.2386467179014296,"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."}}