{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000231834,0.00007640395,0.0001974932,0.0002673947,0.00002873239,0.000004705845,0.0002912528,0.00004949722,0.000006625904],"category_scores_gemma":[0.0001304742,0.00005752093,0.0000384308,0.0003730315,0.0000701859,0.00004793039,0.00005370974,0.00006236383,4.270961e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002084811,"about_ca_system_score_gemma":0.0001849751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002675244,"about_ca_topic_score_gemma":0.00007500513,"domain_scores_codex":[0.9992707,0.000008273179,0.0002909395,0.00008430189,0.0001301903,0.0002156104],"domain_scores_gemma":[0.9995016,0.0002632783,0.00006464293,0.0001125716,0.00004783984,0.0000100842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001896493,0.00008147128,0.1502964,0.004825786,0.0002915877,8.910991e-7,0.004066549,0.00586044,0.329085,0.06469023,0.0004817834,0.4401303],"study_design_scores_gemma":[0.0004704096,0.00006261354,0.04681378,0.0002492406,0.000004173541,2.697715e-7,0.001177595,0.04322741,0.8929558,0.01414107,0.0007870843,0.0001105342],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947374,0.0001088358,0.003818627,0.0001403637,0.00004574637,0.0003769654,0.00001939647,0.00005599068,0.0006966274],"genre_scores_gemma":[0.997067,0.00004976576,0.002709267,0.000001409454,0.000005807167,0.0001147844,0.000003395843,0.000009901255,0.00003870253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5638708,"threshold_uncertainty_score":0.2345636,"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."}}