{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009800592,0.001240485,0.001436069,0.0003782218,0.0008055358,0.000480369,0.0009634263,0.001194321,0.001021915],"category_scores_gemma":[0.0007126373,0.001358756,0.0005802237,0.0008672294,0.0005165429,0.0006325695,0.0008881609,0.0007592019,0.0002908035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009068967,"about_ca_system_score_gemma":0.000132253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002888373,"about_ca_topic_score_gemma":0.003997731,"domain_scores_codex":[0.9941423,0.00008166058,0.001187436,0.001481061,0.0006912045,0.002416332],"domain_scores_gemma":[0.9964253,0.0009377592,0.0002518471,0.001566279,0.0002636976,0.0005550983],"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":[0.0006299909,0.0004523534,0.06231375,0.004619504,0.002605809,0.0005071248,0.005031266,0.000723255,0.00546494,0.04360878,0.7628191,0.1112241],"study_design_scores_gemma":[0.005922955,0.00117507,0.1402531,0.0008391467,0.000742927,0.0001301124,0.006490259,0.02305731,0.008300488,0.02155262,0.7869678,0.004568231],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.944007,0.01098135,0.002453661,0.003190885,0.007466749,0.002723282,0.001990866,0.007419759,0.01976646],"genre_scores_gemma":[0.9794825,0.006783343,0.002131671,0.0003189816,0.001669078,0.0005539627,0.0002741948,0.0003581921,0.00842812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1066559,"threshold_uncertainty_score":0.9998913,"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."}}