{"id":"W2903046240","doi":"10.1016/j.trpro.2019.09.073","title":"Calculation of potential for setting up charging infrastructure for battery-powered electric vehicles – Focusing the calculation of potential according to the urban quarter level","year":2019,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Battery (electricity); Electric vehicle; Automotive engineering; Electric cars; Engineering; Computer science; Electrical engineering; Power (physics); Physics; Geography","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":[0.0002585696,0.00045223,0.0003793381,0.0009844867,0.0004892509,0.001026181,0.0008593299,0.0006573247,0.007170307],"category_scores_gemma":[0.001095064,0.0002755956,0.0007053891,0.001189554,0.000317791,0.0009285866,0.0006217815,0.0004034063,0.000758567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007855945,"about_ca_system_score_gemma":0.000937684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007104271,"about_ca_topic_score_gemma":0.00494401,"domain_scores_codex":[0.9998087,0.00003498884,0.000007103141,0.0000178918,0.00007900346,0.00005228866],"domain_scores_gemma":[0.9997838,0.00006235643,0.00001454828,0.00002059861,0.0001019378,0.00001674558],"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":[0.00009165324,0.00004186414,0.0049397,0.000268362,0.00003848482,0.0004151469,0.00009863951,0.9083564,0.005641016,0.04461366,0.002418287,0.03307661],"study_design_scores_gemma":[0.00001273127,0.00005261764,0.003010841,0.00003668532,0.00002317685,0.0001321104,0.0002682265,0.9711846,0.003959029,0.01499369,0.00630754,0.00001859753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3161564,0.0009140674,0.4748798,0.0005169837,0.0002069911,0.0002586614,0.001736369,0.0007568687,0.2045738],"genre_scores_gemma":[0.9673795,0.0003146555,0.02075363,0.00003112803,0.00001419696,0.00008542671,0.0004305399,0.0001160215,0.01087493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007170307,"threshold_uncertainty_score":0.02398711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01604226983472763,"score_gpt":0.2791940890955435,"score_spread":0.2631518192608159,"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."}}