{"id":"W4416136229","doi":"10.1109/pesgm52009.2025.11225073","title":"Assessing Electric Vehicle Charging Diversity Based on AMI and Vehicle Telematics Data","year":2025,"lang":"","type":"article","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro One (Canada)","funders":"","keywords":"Telematics; Electric vehicle; Metering mode; Service (business); Charging station; Diversity (politics); Distribution transformer","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005726677,0.0004165449,0.0003222958,0.00161519,0.000518823,0.0006548127,0.0005796988,0.0003599222,0.0009784097],"category_scores_gemma":[0.00326466,0.0001321362,0.0003270697,0.003569498,0.0003557535,0.0005258627,0.0006294199,0.0003667462,0.0003082875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004260158,"about_ca_system_score_gemma":0.003152534,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7807069,"about_ca_topic_score_gemma":0.8598929,"domain_scores_codex":[0.9993986,0.00008776098,0.00003306754,0.0001034752,0.0002494138,0.0001276773],"domain_scores_gemma":[0.9979007,0.0004354338,0.0002228768,0.0001911307,0.001088933,0.0001608101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005873886,0.0001652176,0.8676983,0.0002771739,0.0003549442,0.0003777252,0.000720353,0.07160107,0.002880591,0.001632288,0.01649586,0.03720909],"study_design_scores_gemma":[0.00003255737,0.00005889625,0.9025468,0.00004254064,0.00006577504,0.0001562437,0.001505601,0.07900776,0.001561552,0.0003614813,0.01461149,0.0000490743],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9388997,0.0001618252,0.001934366,0.0002063422,0.00001148298,0.00005717673,0.05447442,0.0001696695,0.004085001],"genre_scores_gemma":[0.9331914,0.0001274474,0.002103418,0.00002628472,0.00001068094,0.0000321389,0.06354842,0.00001539552,0.0009448124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7807069,"threshold_uncertainty_score":0.4411691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01774349055796577,"score_gpt":0.247929960835014,"score_spread":0.2301864702770482,"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."}}