{"id":"W2806004584","doi":"10.1109/tie.2018.2840502","title":"Optimized <italic>LCC</italic>-Series Compensated Resonant Network for Stationary Wireless EV Chargers","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Wireless Power Transfer Systems","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Voltage; Electronic engineering; Power (physics); Electrical engineering; Engineering; Range (aeronautics); Compensation (psychology); Series (stratigraphy); Topology (electrical circuits); Control theory (sociology); Computer science; Physics","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":[0.0001656643,0.0004822803,0.0002691615,0.0002492967,0.0002234507,0.0004446634,0.0006433623,0.0003325523,0.002420287],"category_scores_gemma":[0.0002161787,0.0001707995,0.0003056921,0.0002016119,0.0001871719,0.0004594092,0.0001746712,0.0002026313,0.0007711158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005775474,"about_ca_system_score_gemma":0.000305632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007311528,"about_ca_topic_score_gemma":0.001726623,"domain_scores_codex":[0.9998622,0.00001992697,0.000005841431,0.00003254806,0.00006384541,0.00001552665],"domain_scores_gemma":[0.9999382,0.000007505961,0.00001889968,0.000006845174,0.00002568455,0.000002759088],"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":[0.0004295164,0.00012887,0.001036116,0.0004612774,0.00007869407,0.0002467374,0.0001169684,0.184596,0.6330174,0.02002249,0.004116022,0.15575],"study_design_scores_gemma":[0.00005999461,0.0005994871,0.001407347,0.00003630295,0.00009129156,0.0004596721,0.00008541571,0.776591,0.1950724,0.002713543,0.02283317,0.0000503061],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09221102,0.0005134137,0.8868775,0.0001593333,0.00007370597,0.0001122326,0.0001158602,0.0007665536,0.01917044],"genre_scores_gemma":[0.8588154,0.0002119441,0.133021,0.00005601527,0.00002363865,0.00009559601,0.0001172069,0.0001073438,0.007551803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002420287,"threshold_uncertainty_score":0.008096635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02264941696014727,"score_gpt":0.2297131028451883,"score_spread":0.207063685885041,"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."}}