{"id":"W4413917485","doi":"10.1109/icra55743.2025.11128138","title":"Advanced $X \\theta$ Reluctance Electromagnetic Micropositioning System for Precision Motion Control","year":2025,"lang":"en","type":"article","venue":"","topic":"Magnetic Bearings and Levitation Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion control; Motion (physics); Reluctance motor; Magnetic reluctance; Switched reluctance motor; Physics; Control theory (sociology); Computer science; Control system; Control engineering; Control (management); Electrical engineering; Engineering; Torque; Artificial intelligence; Classical mechanics; Magnet; Robot","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":[],"consensus_categories":[],"category_scores_codex":[0.0001028132,0.0001027967,0.0001343272,0.00007896849,0.00007834992,0.00004317842,0.00007591043,0.00005763049,0.000024391],"category_scores_gemma":[0.00002096782,0.0001038071,0.00005059321,0.0001427657,0.000009676469,0.00006948548,0.000005362354,0.00006653611,0.000008252819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009530861,"about_ca_system_score_gemma":0.000008628557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003067535,"about_ca_topic_score_gemma":0.000005188738,"domain_scores_codex":[0.9993823,0.00001223516,0.0002183423,0.0001479935,0.00006373723,0.0001753552],"domain_scores_gemma":[0.9996417,0.00009064771,0.00002488504,0.0001368265,0.00008052128,0.00002546562],"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":[0.0001178814,0.0000338197,0.0001047985,0.001132335,0.0000745101,0.000001160724,0.000114576,0.1511623,0.6158454,0.1042145,0.001322167,0.1258764],"study_design_scores_gemma":[0.001480143,0.0001031064,0.00131545,0.0001669298,0.0000413204,0.0000038458,0.00008231392,0.9851768,0.008268456,0.001096909,0.002098821,0.0001659179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07349054,0.000572087,0.9047316,0.0001352797,0.0004124789,0.0005986421,0.000007456517,0.0004406338,0.01961126],"genre_scores_gemma":[0.97215,0.00001889706,0.02530432,0.00004697512,0.0000228873,0.000111969,0.00001166266,0.00001672369,0.002316538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8986595,"threshold_uncertainty_score":0.4233132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002008752256185983,"score_gpt":0.1916575081805694,"score_spread":0.1896487559243834,"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."}}