{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001664595,0.0002084728,0.0002507668,0.00009184023,0.0001058103,0.0001015231,0.0007655482,0.0002339407,0.0001951267],"category_scores_gemma":[0.00003736455,0.0002293343,0.0001630026,0.0001310967,0.0000357344,0.00006494391,0.0006250259,0.0004747353,0.00002298932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009526439,"about_ca_system_score_gemma":0.00006991994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000133749,"about_ca_topic_score_gemma":0.00006473127,"domain_scores_codex":[0.9991772,0.00003270145,0.0003369166,0.0002132231,0.00007720575,0.0001628169],"domain_scores_gemma":[0.9970617,0.0001711763,0.00005993065,0.00226003,0.0003837602,0.00006342189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004826003,0.002816311,0.0002782525,0.003880259,0.00366892,0.000003252523,0.00925564,0.5839141,0.007495321,0.1270682,0.1720391,0.08953236],"study_design_scores_gemma":[0.0008258101,0.00005198419,0.0008343727,0.0004198293,0.000204473,0.000005680723,0.0009413893,0.3491091,0.006899363,0.01199241,0.6271957,0.00151988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00297088,0.01094163,0.9132545,0.001214713,0.0002937822,0.001682447,0.0002344886,0.001268715,0.06813886],"genre_scores_gemma":[0.8392637,0.002913942,0.1501286,0.0001384647,0.00007477796,0.00354918,0.00267371,0.00009047068,0.001167177],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8362928,"threshold_uncertainty_score":0.9351983,"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."}}