{"id":"W4285803959","doi":"10.3390/s22145320","title":"Power Line Communication and Sensing Using Time Series Forecasting","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Bundesministerium für Bildung und Forschung; Bergische Universität Wuppertal","keywords":"Power-line communication; Smart grid; Computer science; Electronic engineering; Gaussian; Real-time computing; Telecommunications network; Engineering; Power (physics); Data mining; Electrical engineering; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000786831,0.0007302451,0.0004624453,0.0006171656,0.0003152797,0.0006090417,0.0005854724,0.0006037819,0.0006788394],"category_scores_gemma":[0.003328962,0.0002153328,0.0003880863,0.0008223476,0.0002410484,0.0009411393,0.0003284202,0.000765266,0.00022105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000484314,"about_ca_system_score_gemma":0.0004139691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007720601,"about_ca_topic_score_gemma":0.006118389,"domain_scores_codex":[0.9996208,0.00009529317,0.00002947049,0.00009804301,0.0001291687,0.00002728264],"domain_scores_gemma":[0.9988863,0.000694061,0.0001575579,0.0000846908,0.0001508634,0.00002651595],"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":[0.00006803078,0.00005672791,0.003447461,0.00003668093,0.00003539469,0.000085572,0.00003778713,0.8860441,0.003057648,0.003083952,0.0007936708,0.103253],"study_design_scores_gemma":[0.000001090121,0.000006098724,0.0002087047,0.000001844388,0.00000256741,0.00000773888,0.000003427145,0.9985526,0.0004423226,0.0006516458,0.0001196375,0.000002144743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07170174,0.0003604091,0.9236869,0.0004000466,0.00007600126,0.00004380351,0.0001534375,0.001044836,0.002532932],"genre_scores_gemma":[0.9043356,0.000437184,0.09379379,0.00006266085,0.00007384738,0.00004520442,0.0002599965,0.00004199354,0.0009498069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007720601,"threshold_uncertainty_score":0.0153513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02243634174230264,"score_gpt":0.2248295125909514,"score_spread":0.2023931708486487,"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."}}