{"id":"W4362574740","doi":"10.1155/2023/6103796","title":"Optimal Deployment of Electric Vehicles’ Fast-Charging Stations","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Software deployment; Charging station; Installation; Context (archaeology); Electric vehicle; Driving range; Transport engineering; Investment (military); Computer science; Integer programming; Range (aeronautics); Operations research; Environmental economics; Engineering; Power (physics); Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0008296383,0.001305295,0.001089452,0.0006005141,0.0006427563,0.001412954,0.001183237,0.001172884,0.00285899],"category_scores_gemma":[0.002498191,0.0008393611,0.0007648639,0.0007753788,0.0005016795,0.001675071,0.001103497,0.0006547266,0.0003983255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001619379,"about_ca_system_score_gemma":0.001715066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01202229,"about_ca_topic_score_gemma":0.008760365,"domain_scores_codex":[0.9994501,0.000157321,0.00002348613,0.0001237415,0.00006305784,0.000182281],"domain_scores_gemma":[0.9990061,0.0004335077,0.0001994498,0.00006590896,0.000167284,0.0001276809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007401282,0.00003371396,0.0007318342,0.00003988099,0.00001248305,0.00005807848,0.00002436793,0.9885281,0.001213669,0.001688799,0.0004499245,0.00714521],"study_design_scores_gemma":[0.00001441672,0.00005939856,0.0003529094,0.000005648669,0.000016537,0.00001686785,0.00009028652,0.9970924,0.0007946601,0.001181032,0.0003678749,0.000007897655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5064662,0.0007251039,0.477992,0.0006568747,0.0001087471,0.0002567135,0.0005219929,0.000596832,0.01267558],"genre_scores_gemma":[0.9665594,0.000245239,0.03085955,0.00002831297,0.000008901146,0.00006559724,0.0001726875,0.00002784258,0.002032524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01202229,"threshold_uncertainty_score":0.02390462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005420747581623713,"score_gpt":0.2219524256738261,"score_spread":0.2165316780922024,"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."}}