{"id":"W4406071987","doi":"10.1016/j.geits.2025.100255","title":"Understanding spatial–temporal attributes influencing electric vehicle's charging stations utilization: A multi-city study","year":2025,"lang":"en","type":"article","venue":"Green Energy and Intelligent Transportation","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Electric vehicle; Transport engineering; Computer science; Environmental science; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001862567,0.0003970117,0.0005386388,0.001449116,0.001315659,0.002122646,0.000888924,0.0006103055,0.002198976],"category_scores_gemma":[0.004202322,0.0005014971,0.001922685,0.003422777,0.0006047976,0.001458142,0.00152849,0.0008118118,0.0003929871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002076593,"about_ca_system_score_gemma":0.002082251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1878025,"about_ca_topic_score_gemma":0.2899192,"domain_scores_codex":[0.9987854,0.000366281,0.0001017298,0.0002105364,0.0002078826,0.0003281969],"domain_scores_gemma":[0.9963945,0.0009883018,0.001063674,0.0003174728,0.0007674459,0.0004686829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005820118,0.0001149719,0.9952024,0.00002356709,0.0001335766,0.0001693232,0.001683245,0.0002169776,0.0000965963,0.00007674369,0.0001653283,0.00205893],"study_design_scores_gemma":[0.00000441677,0.0000935085,0.9892314,0.00002462555,0.0001064834,0.0001017125,0.00882994,0.0009166154,0.00005071983,0.00004516164,0.0005787236,0.00001666801],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991797,0.00007106682,0.000172704,0.00004217651,0.000003280075,0.00001566566,0.0002400205,0.000001759771,0.0002736505],"genre_scores_gemma":[0.9989296,0.000117421,0.0002322565,0.00002598857,0.000004536333,0.00002477773,0.0003861088,0.000003396708,0.0002758932],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1878025,"threshold_uncertainty_score":0.3734187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05460188697271413,"score_gpt":0.2518051142027977,"score_spread":0.1972032272300836,"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."}}