{"id":"W1999704582","doi":"10.1109/isplc.2014.6812364","title":"Adaptive impedance matching for Vehicular Power Line Communication systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Impedance matching; Electrical impedance; Computer science; Matching (statistics); Electronic engineering; Output impedance; SIGNAL (programming language); Power (physics); Impedance bridging; Standing wave ratio; Communications system; Characteristic impedance; Electrical engineering; Damping factor; Antenna (radio); Engineering; Telecommunications; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003093541,0.0000951179,0.0001273971,0.00004037786,0.00009371516,0.0000353217,0.0003105715,0.00004925494,0.000009798216],"category_scores_gemma":[0.0000251356,0.00008875356,0.00004580088,0.00007241018,0.00001499895,0.0001130289,0.00005012272,0.0001054752,0.00002810515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003164346,"about_ca_system_score_gemma":0.000005176363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005244288,"about_ca_topic_score_gemma":0.00002249324,"domain_scores_codex":[0.9994961,0.00003963835,0.0001910741,0.00007959708,0.00005880416,0.0001348051],"domain_scores_gemma":[0.9989149,0.0001855597,0.00002933876,0.0007579376,0.00007195136,0.00004028784],"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":[0.00003164999,0.0001097346,0.00007744966,0.0001311988,0.0001719421,2.753886e-7,0.001522695,0.5665756,0.009179647,0.403242,0.01137031,0.007587523],"study_design_scores_gemma":[0.0002693059,0.00004305842,0.0001177735,0.00005547189,0.00001078175,0.000001821601,0.0002030534,0.951722,0.0004722364,0.001300116,0.04564603,0.0001583101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07912379,0.003729151,0.8889688,0.0002266877,0.0002470228,0.000421839,0.00001345353,0.000536917,0.02673236],"genre_scores_gemma":[0.9799969,0.0001379666,0.01941347,0.00004174899,0.00003413656,0.0001008626,0.00002373282,0.00002757571,0.0002236472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9008731,"threshold_uncertainty_score":0.3619265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436339924208601,"score_gpt":0.2358718817645936,"score_spread":0.2215084825225076,"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."}}