{"id":"W2057030616","doi":"10.1109/tmag.2013.2245675","title":"Modeling and Analysis of Eddy-Current Damping Effect in Horizontal Motions for a High-Precision Magnetic Navigation Platform","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Micro and Nano Robotics","field":"Physics and Astronomy","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Eddy current; Magnetic levitation; Levitation; Magnetic field; Damper; Magnetic damping; Maglev; Eddy current brake; Electrical conductor; Electrodynamic suspension; Physics; Mechanics; Acoustics; Mechanical engineering; Vibration; Computer science; Electrical engineering; Magnet; Engineering; Magnetic energy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002091701,0.0004479918,0.0002760313,0.0002177204,0.0002617293,0.000469766,0.0004141416,0.000755909,0.0008227027],"category_scores_gemma":[0.0004573035,0.0002526228,0.0004050302,0.0001220446,0.0003870938,0.0004567829,0.0002388212,0.0003172238,0.0002021001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004286238,"about_ca_system_score_gemma":0.0005117918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005810895,"about_ca_topic_score_gemma":0.003327779,"domain_scores_codex":[0.9999294,0.00001518504,0.00000272413,0.00001311822,0.00003053756,0.000009058138],"domain_scores_gemma":[0.999867,0.00005506806,0.00003182323,0.000009180689,0.00002878232,0.000008276416],"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":[0.00004508766,0.00003092479,0.001017499,0.00005481349,0.00001610188,0.0002390568,0.0001160256,0.9481841,0.03893089,0.004315734,0.0002189092,0.006830838],"study_design_scores_gemma":[0.000001959945,0.00001220687,0.0001490311,0.000001643253,0.000001761652,0.000009796531,0.000004725764,0.9988967,0.0006672396,0.0001293756,0.0001236522,0.000001903298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2212669,0.0005338137,0.7680942,0.000224246,0.00003424541,0.00005787134,0.00004812737,0.0003981479,0.009342499],"genre_scores_gemma":[0.9795139,0.0002015244,0.01585762,0.0000184342,0.000008396714,0.00004086847,0.00002693578,0.00003813366,0.004294204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005810895,"threshold_uncertainty_score":0.01155412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0116917712444549,"score_gpt":0.2516621489387404,"score_spread":0.2399703776942855,"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."}}