{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003980246,0.00016457,0.0001750626,0.0001763474,0.00007406796,0.0001575564,0.0004356866,0.0000768544,0.00006660126],"category_scores_gemma":[0.00005945818,0.0001699078,0.00002513407,0.0005114194,0.000009691412,0.0005522192,0.00009702466,0.0004326842,0.00001978623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008805915,"about_ca_system_score_gemma":0.0000399247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004863189,"about_ca_topic_score_gemma":0.00000878191,"domain_scores_codex":[0.9987309,0.00001281796,0.000277351,0.0003519737,0.0001523984,0.000474552],"domain_scores_gemma":[0.999377,0.0001031158,0.00001870467,0.0004319617,0.000009898316,0.00005933111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000170515,0.0000101164,0.0001388723,0.0004885235,0.0001269462,0.00006461109,0.0009699818,0.09223579,0.01564515,0.001190954,0.1571473,0.7319647],"study_design_scores_gemma":[0.0002015646,0.00003276878,0.0001337939,0.00004965799,0.00001116289,0.00001091384,0.00003886192,0.6659096,0.0006153958,0.00007338532,0.3327762,0.0001466573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.17862,0.03669712,0.7368613,0.001752261,0.004895447,0.004081129,0.0252173,0.004681962,0.007193454],"genre_scores_gemma":[0.9641647,0.0004153341,0.002682182,0.0001978935,0.0005397747,0.00005334152,0.03174215,0.0000904171,0.0001142125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7855446,"threshold_uncertainty_score":0.6928638,"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."}}