{"id":"W4392526028","doi":"10.26480/aem.02.2023.62.66","title":"OPTIMAL PLACEMENT AND SIZING OF ELECTRIC VEHICLE CHARGING INFRASTRUCTURE USING DC POWER FLOW MODEL","year":2023,"lang":"en","type":"article","venue":"Acta Electronica Malaysia","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sizing; Power flow; Automotive engineering; Electric vehicle; Power (physics); Flow (mathematics); Electrical engineering; Computer science; Engineering; Electric power system; Physics; Mechanics","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":[0.0002597599,0.000724095,0.0006554121,0.000483105,0.0004960383,0.001157888,0.0007518972,0.0008478743,0.00526416],"category_scores_gemma":[0.0007318541,0.0005665835,0.000473815,0.0007641533,0.0005257472,0.0003966506,0.0004125808,0.0005628075,0.0003548348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003052198,"about_ca_system_score_gemma":0.002714534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1952381,"about_ca_topic_score_gemma":0.1895805,"domain_scores_codex":[0.9998869,0.00002803646,0.000003491589,0.00002553659,0.00002235338,0.00003370907],"domain_scores_gemma":[0.9997627,0.0001166575,0.00003091094,0.000007456887,0.00006251201,0.00001977448],"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":[0.00001157478,0.000005367292,0.000160504,0.00001035149,0.000002476074,0.00001774679,0.000005579739,0.9974153,0.0001143443,0.0008453549,0.0002483557,0.001163098],"study_design_scores_gemma":[0.000004512169,0.00000639416,0.0001093286,0.000002508049,0.000002519256,0.000002242723,0.00001058879,0.9991692,0.00004690162,0.0003487148,0.000295399,0.000001661832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2411097,0.001139828,0.6311511,0.001177972,0.0001632018,0.0004528311,0.002884534,0.0005479172,0.1213729],"genre_scores_gemma":[0.9606884,0.0003190646,0.02194866,0.00004960021,0.00001672975,0.0001134869,0.000450199,0.000033725,0.01638007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1952381,"threshold_uncertainty_score":0.3882034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005939004560601371,"score_gpt":0.2078850057191283,"score_spread":0.201946001158527,"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."}}