{"id":"W4389352631","doi":"10.1109/access.2023.3340131","title":"EV Charging Profiles and Waveforms Dataset (EV-CPW) and Associated Power Quality Analysis","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Electrical engineering; Computer science; Voltage; Power electronics; Power (physics); Waveform; Reliability (semiconductor); Electric vehicle; Renewable energy; Harmonics; Electronics; Electronic engineering; Engineering; Physics","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":[0.0007537704,0.001099237,0.0009244573,0.002936457,0.0004330998,0.00112283,0.00170796,0.001631097,0.01571285],"category_scores_gemma":[0.003400077,0.0002908755,0.0009848389,0.004837275,0.0002057622,0.001076247,0.001262426,0.001216587,0.02288536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008245587,"about_ca_system_score_gemma":0.0009169953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01448183,"about_ca_topic_score_gemma":0.02288673,"domain_scores_codex":[0.9992052,0.0000884649,0.0001213965,0.0001556242,0.0003197133,0.0001096977],"domain_scores_gemma":[0.9978278,0.0003781954,0.0001901213,0.0005598867,0.0008996624,0.0001443349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001959397,0.0001407364,0.005153893,0.000910527,0.00006845181,0.0001623887,0.00006015384,0.003194312,0.001262266,0.0008791487,0.962979,0.02499323],"study_design_scores_gemma":[0.0002403876,0.00009110815,0.03438896,0.0002797639,0.00005453219,0.0003347064,0.0002371917,0.01317813,0.0029226,0.002205775,0.9459739,0.00009297902],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002437948,0.0000835304,0.0009258974,0.0001130516,0.00005403743,0.00005241526,0.9927451,0.001594577,0.001993393],"genre_scores_gemma":[0.002513144,0.00005400925,0.0009957259,0.00002917161,0.000009789137,0.00006446485,0.9955351,0.00008332822,0.0007152719],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01571285,"threshold_uncertainty_score":0.05256474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05412363431238208,"score_gpt":0.3728578771456384,"score_spread":0.3187342428332563,"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."}}