{"id":"W2346458564","doi":"10.1109/tvt.2016.2562629","title":"An Adaptive Impedance-Matching System for Vehicular Power Line Communication","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"AUTO21 Network of Centres of Excellence; Natural Sciences and Engineering Research Council of Canada","keywords":"Impedance matching; Electronic engineering; Electrical impedance; Maximum power transfer theorem; Communications system; Power-line communication; Engineering; SIGNAL (programming language); Characteristic impedance; Output impedance; Noise (video); Damping factor; Electrical engineering; Computer science; Power (physics)","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":[0.0002366073,0.0002480579,0.0002843765,0.0004103045,0.0003029741,0.00002167668,0.0006945872,0.0003564813,0.00001507745],"category_scores_gemma":[0.000006408357,0.0002068894,0.0001424976,0.0003673258,0.0001256694,0.0002429798,0.000004280206,0.0003532204,0.00005494505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000226416,"about_ca_system_score_gemma":0.00002522694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000115297,"about_ca_topic_score_gemma":0.00005001633,"domain_scores_codex":[0.9988389,0.00006773361,0.0003652354,0.0002765849,0.0001207123,0.0003308422],"domain_scores_gemma":[0.9978675,0.0001378699,0.00006558263,0.001694946,0.0001554078,0.00007875442],"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":[0.0002122244,0.0006359846,0.00001297226,0.0001381875,0.0006010587,0.00001311984,0.0005147483,0.2226428,0.5984574,0.02260285,0.0001717482,0.1539969],"study_design_scores_gemma":[0.003303976,0.00147477,0.00004040066,0.0008615478,0.000239885,0.0001162375,0.001598628,0.2220235,0.7533204,0.003510804,0.01229821,0.001211668],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2072903,0.0005664911,0.7891223,0.0005903702,0.0002354053,0.0004167689,0.00007956116,0.001572262,0.0001265644],"genre_scores_gemma":[0.9870246,0.0003045345,0.01197124,0.00002497672,0.00001786095,0.000536424,0.000007922964,0.00007577092,0.00003663126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7797344,"threshold_uncertainty_score":0.8436705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01163872325345868,"score_gpt":0.2405810529763445,"score_spread":0.2289423297228858,"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."}}