{"id":"W4361988838","doi":"10.1051/e3sconf/202337604021","title":"Forecast of the development of demand for charging points for electric vehicles in Russian cities","year":2023,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Zero emission; Electric cars; Greenhouse gas; Electric vehicle; Quarter (Canadian coin); Fossil fuel; Environmental economics; Transport engineering; Green vehicle; Scale (ratio); Business; Engineering; Fuel efficiency; Automotive engineering; Economics; Electrical 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":[0.0002555599,0.0004424361,0.0002499195,0.0007751166,0.0002689301,0.0006751138,0.00040386,0.0004539136,0.002710032],"category_scores_gemma":[0.0005580875,0.0001957326,0.0005678788,0.000784192,0.0001034738,0.0006003607,0.0003415683,0.0004867363,0.0009999969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001711259,"about_ca_system_score_gemma":0.000773424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03963958,"about_ca_topic_score_gemma":0.0251283,"domain_scores_codex":[0.9998374,0.00002542069,0.0000120711,0.00002859685,0.00005370373,0.00004276387],"domain_scores_gemma":[0.9996611,0.00004145778,0.00004649786,0.00001168901,0.0001993081,0.00004000263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003347707,0.000198898,0.5525374,0.0004568304,0.0001680051,0.001927968,0.0006768541,0.3364907,0.009339968,0.01485276,0.03367401,0.04934178],"study_design_scores_gemma":[0.00003301628,0.0002125385,0.5898601,0.000102328,0.0001311593,0.0004168923,0.001524855,0.3583437,0.003105068,0.002950497,0.04323629,0.00008354492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9426329,0.0009511156,0.006828933,0.001345932,0.0001178725,0.0000418085,0.01682408,0.0003671074,0.03089027],"genre_scores_gemma":[0.9898844,0.0005798325,0.0007642808,0.00002999444,0.00001663163,0.00001838738,0.004345498,0.00001471034,0.004346317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03963958,"threshold_uncertainty_score":0.07881773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04062913473300388,"score_gpt":0.2820831246081326,"score_spread":0.2414539898751287,"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."}}