{"id":"W4400524361","doi":"10.36535/0235-5019-2023-10-5","title":"SMART CITY TORONTO CITY FOR CITIZENS AND TOURISTS","year":2023,"lang":"ru","type":"article","venue":"Проблемы окружающей среды и природных ресурсов","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Smart city; Advertising; Geography; Media studies; Internet privacy; Business; Sociology; Computer science; Internet of Things","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":[0.0002232364,0.0003482208,0.0001801415,0.0004896307,0.00368559,0.003428721,0.0003387951,0.0006999853,0.08103306],"category_scores_gemma":[0.0004873268,0.0001823421,0.000196609,0.001541113,0.0005808527,0.0008000112,0.001842388,0.0006005533,0.01215533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00515812,"about_ca_system_score_gemma":0.01093313,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4169245,"about_ca_topic_score_gemma":0.7588186,"domain_scores_codex":[0.9997291,0.00003379307,0.000007366468,0.0000332324,0.0001109094,0.00008549129],"domain_scores_gemma":[0.9995607,0.00002760426,0.00002326863,0.00003617362,0.0001557681,0.0001965249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006595278,0.00002284466,0.0103762,0.0002032748,0.000009370772,0.0003525328,0.003251746,0.0002956498,0.0008195548,0.02461964,0.8841348,0.0758484],"study_design_scores_gemma":[0.000006922118,0.000009000943,0.01128766,0.00005447347,0.000005075232,0.00005925222,0.002320318,0.0001553185,0.0001858605,0.0006002418,0.9853034,0.00001240456],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03182432,0.002440423,0.001810173,0.01387235,0.001296758,0.0002363964,0.01816888,0.0007179559,0.9296328],"genre_scores_gemma":[0.142238,0.002920459,0.005091219,0.00106126,0.0002986499,0.0002088295,0.009487925,0.0002626204,0.8384309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5830755,"threshold_uncertainty_score":0.8289955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0249424204167661,"score_gpt":0.2456136770913134,"score_spread":0.2206712566745473,"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."}}