{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001765747,0.00008257275,0.00009548428,0.00006980172,0.0003609418,0.00002581289,0.0001035789,0.00001942786,0.00008060087],"category_scores_gemma":[0.00002412003,0.00009740428,0.00002128897,0.0001543562,0.00003462817,0.00008360714,0.0002022777,0.000187629,0.000004500855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000496641,"about_ca_system_score_gemma":0.000007114339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001689287,"about_ca_topic_score_gemma":0.000004646326,"domain_scores_codex":[0.9995171,0.00006713042,0.0001421275,0.00007467983,0.00007191621,0.0001269815],"domain_scores_gemma":[0.9994785,0.00006284175,0.00002854735,0.0003745962,0.0000237078,0.00003186621],"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":[0.00005026191,0.00006182071,0.0006142835,0.00006790949,0.0001428882,0.00002881383,0.008160325,0.9011492,0.07324968,0.0007653148,0.001521093,0.01418847],"study_design_scores_gemma":[0.0001295961,0.00002411849,0.0001189948,0.00001738275,0.00001102168,0.0001464551,0.0005184975,0.9853897,0.0008810484,0.000170638,0.01243282,0.0001597046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943148,0.0008604347,0.0001832251,0.0001636712,0.00006404879,0.000072464,0.00001230958,0.0001803837,0.004148589],"genre_scores_gemma":[0.9855786,0.00005172884,0.01403982,0.00002512085,0.00001406906,0.000001578455,0.00001854197,0.0000287722,0.0002417768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08424057,"threshold_uncertainty_score":0.3972031,"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."}}