{"id":"W4377097971","doi":"10.3390/en16104186","title":"Locating and Sizing Electric Vehicle Chargers Considering Multiple Technologies","year":2023,"lang":"en","type":"article","venue":"Energies","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Group for Research in Decision Analysis; HEC Montréal","funders":"European Commission","keywords":"Sizing; Solver; Heuristic; Electric vehicle; Computer science; Set (abstract data type); Time horizon; Investment (military); Order (exchange); Mathematical optimization; Operations research; Real-time computing; Automotive engineering; Engineering; Business; Finance; Power (physics); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0000670274,0.0001293482,0.0001327695,0.0001846514,0.0001198333,0.00004896674,0.00008759432,0.00008595084,0.000004247177],"category_scores_gemma":[0.00009670671,0.0001263321,0.00002269944,0.0006395637,0.00002975034,0.0001217797,0.00003685369,0.000174448,0.00001334992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002729279,"about_ca_system_score_gemma":0.000007828452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001460865,"about_ca_topic_score_gemma":0.000005596383,"domain_scores_codex":[0.9992925,0.000006186819,0.0001303599,0.0001468523,0.00008215508,0.0003419569],"domain_scores_gemma":[0.9997036,0.0001152264,0.00001854763,0.0001236583,0.00001430088,0.00002466338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001913072,0.000001767059,0.007714574,0.00008969782,0.00003160283,0.00001437575,0.0003521096,0.1251718,0.7380573,0.0006710435,0.001123252,0.1267706],"study_design_scores_gemma":[0.0002281177,0.00002492245,0.006344627,0.00003088348,0.000008240399,0.00001528013,0.00127975,0.4318359,0.5563328,0.001063968,0.002538971,0.0002964537],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99274,0.003407327,0.00007738821,0.0001195432,0.0001008731,0.00005047072,0.000001228987,0.0030155,0.0004876066],"genre_scores_gemma":[0.9984587,0.0007886882,0.0006019585,0.00001808364,0.00004104772,0.00001545458,0.00000267267,0.00003132834,0.00004208539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3066642,"threshold_uncertainty_score":0.5151674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007085067397836216,"score_gpt":0.185573723621119,"score_spread":0.1784886562232828,"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."}}