{"id":"W3196194646","doi":"10.32920/ryerson.14654949.v1","title":"Power Line Communication For Automotive Applications","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Institute of Mental Health; Shandong Academy of Sciences","keywords":"Automotive industry; Power-line communication; Automotive engineering; Noise (video); Power (physics); Engineering; Electric power transmission; Electrical engineering; Battery (electricity); Electronic engineering; Line (geometry); Computer science","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":[0.0001828676,0.0005225704,0.000221421,0.0004711097,0.0003806728,0.001115269,0.0004992649,0.0008791844,0.02222896],"category_scores_gemma":[0.0006078476,0.0001361513,0.0002160359,0.001038706,0.0002509507,0.001093642,0.0005082427,0.000744513,0.01045166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003369088,"about_ca_system_score_gemma":0.0003728321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003985133,"about_ca_topic_score_gemma":0.0003294208,"domain_scores_codex":[0.9996619,0.00004599507,0.00001508154,0.00005829769,0.0001890689,0.00002948211],"domain_scores_gemma":[0.9996749,0.00006423675,0.00002822378,0.00005562744,0.000162935,0.00001412379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000124387,0.00008604717,0.0006872908,0.001252528,0.00002536496,0.0004840521,0.0003010666,0.005170707,0.0888937,0.05157002,0.0454741,0.8059308],"study_design_scores_gemma":[0.00001806165,0.0002842119,0.0009300153,0.0003056266,0.00003464219,0.001365922,0.0001937622,0.01041014,0.03986906,0.01976955,0.9267904,0.00002857306],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03620816,0.06705543,0.5726163,0.004237351,0.002974408,0.0003102497,0.0007273136,0.004282146,0.3115886],"genre_scores_gemma":[0.4534949,0.06886699,0.1488131,0.002090949,0.002609573,0.0004373148,0.002012294,0.0007683366,0.3209065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02222896,"threshold_uncertainty_score":0.07436329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02004255195410445,"score_gpt":0.2779780475837991,"score_spread":0.2579354956296946,"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."}}