{"id":"W4399284071","doi":"10.1016/j.dib.2024.110587","title":"Daily electric vehicle charging dataset for training reinforcement learning algorithms","year":2024,"lang":"en","type":"article","venue":"Data in Brief","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reinforcement learning; Machine learning; Kernel density estimation; Constraint (computer-aided design); Artificial intelligence; Electric vehicle; Kernel (algebra); Training (meteorology); Data mining; Power (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":[0.001215014,0.0008076502,0.0005076235,0.001007674,0.0004581369,0.00061176,0.001777836,0.00111845,0.00614998],"category_scores_gemma":[0.004252817,0.0002334121,0.0007871797,0.001208605,0.000370191,0.0008245422,0.0008400965,0.001592232,0.004004936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008658242,"about_ca_system_score_gemma":0.0008516175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006660578,"about_ca_topic_score_gemma":0.01775803,"domain_scores_codex":[0.9993564,0.0001532068,0.00006173595,0.0001536194,0.0001945633,0.00008046626],"domain_scores_gemma":[0.9984408,0.0004389098,0.00009145741,0.0005091962,0.000445434,0.00007424862],"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":[0.0004741824,0.001351442,0.02071002,0.0008186737,0.0002037153,0.0004973277,0.0001653493,0.2351885,0.004338663,0.009028802,0.5874919,0.1397316],"study_design_scores_gemma":[0.0002729259,0.0005607471,0.02289319,0.000181792,0.00004624364,0.0005076011,0.0003500562,0.6943318,0.01481402,0.01467469,0.251242,0.0001250763],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2866979,0.001589189,0.1311759,0.003297954,0.00172158,0.001866058,0.5151153,0.02157837,0.03695787],"genre_scores_gemma":[0.3188245,0.0004391606,0.08796341,0.0006256505,0.000134083,0.001120241,0.5818043,0.000576395,0.008512385],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.006660578,"threshold_uncertainty_score":0.02057374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02470271068315877,"score_gpt":0.2610362071210172,"score_spread":0.2363334964378584,"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."}}