{"id":"W2016804959","doi":"10.1109/tasc.2012.2183871","title":"Design and Optimization of Superconducting MRI Magnet Systems With Magnetic Materials","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Applied Superconductivity","topic":"Superconducting Materials and Applications","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Magnet; Superconducting magnet; Electromagnetic coil; Finite element method; Yoke (aeronautics); Nuclear magnetic resonance; Computer science; Physics; Optimal design; Magnetic field; Superconductivity; Mechanical engineering; Homogeneity (statistics); Materials science; Topology (electrical circuits); Condensed matter physics; Mechanics; Electrical engineering; Engineering","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":[0.0006295129,0.0008663367,0.0006369244,0.0004762564,0.0003095123,0.000661478,0.000523751,0.0006975384,0.002650129],"category_scores_gemma":[0.001445367,0.0006085815,0.0004799147,0.0002996697,0.0006741391,0.000532106,0.0006734341,0.0004562054,0.0005542017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005537907,"about_ca_system_score_gemma":0.001001715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000700357,"about_ca_topic_score_gemma":0.001256575,"domain_scores_codex":[0.9996473,0.0001132593,0.00001234533,0.00005631714,0.0001380418,0.00003276415],"domain_scores_gemma":[0.9996476,0.0001583121,0.0000731516,0.00001899966,0.00007760207,0.00002435578],"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":[0.00007762976,0.00003953198,0.0003361484,0.0002217341,0.00003648984,0.00008199095,0.00006138642,0.9335762,0.01755234,0.0107367,0.001059069,0.03622085],"study_design_scores_gemma":[0.00003416708,0.0002238254,0.0002679337,0.00002358254,0.00002806715,0.00004560381,0.00002931962,0.9835417,0.00416022,0.004909175,0.006721776,0.00001481204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02705311,0.0007022894,0.9603313,0.000242525,0.00008062726,0.0001399106,0.00006367492,0.0003823868,0.01100422],"genre_scores_gemma":[0.4670912,0.0007126545,0.5257495,0.0001214872,0.00005049476,0.0005352731,0.0001395209,0.000229368,0.005370576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002650129,"threshold_uncertainty_score":0.008865595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02159517792684671,"score_gpt":0.2047900849414662,"score_spread":0.1831949070146195,"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."}}