{"id":"W4383619809","doi":"10.1007/978-3-031-32828-2","title":"Data and AI Driving Smart Cities","year":2023,"lang":"en","type":"book","venue":"Studies in big data","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Canada Excellence Research Chairs, Government of Canada; Natural Sciences and Engineering Research Council of Canada; Arizona State University","keywords":"Incentive; Smart city; Computer science; Computer security; Regional science; Data science; Business; Geography; Economics; Internet of Things; Microeconomics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.000291317,0.000331615,0.0005428845,0.0002620406,0.0000895541,0.00005597398,0.001913331,0.0002280638,0.000004930539],"category_scores_gemma":[0.000370434,0.0003219161,0.00001576849,0.0001279539,0.0003905425,0.0002190674,0.01184877,0.0005359438,0.00003493047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009405232,"about_ca_system_score_gemma":0.00003853588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001844426,"about_ca_topic_score_gemma":0.003101908,"domain_scores_codex":[0.9985575,0.000008652094,0.000313407,0.0006005638,0.0001779397,0.0003419501],"domain_scores_gemma":[0.9965442,0.0002885777,0.00003870874,0.00308496,0.00002187605,0.00002165925],"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":[7.299879e-7,0.000001511583,0.000419626,0.0005542161,0.0003199888,0.00005828073,0.0001887643,0.00000701532,2.830308e-7,0.0003999868,0.9863174,0.01173221],"study_design_scores_gemma":[0.0001221864,0.00001092808,0.0002046934,0.0007747245,0.00005744063,0.000006385448,0.003104473,0.0008340853,0.000001308295,0.007526791,0.9869576,0.0003993663],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"review","genre_scores_codex":[0.002358962,0.4165995,0.0006127427,0.003880813,0.05074968,0.002072356,0.08628483,0.01396376,0.4234773],"genre_scores_gemma":[0.005345981,0.5162104,0.001172814,0.0003100922,0.003843109,0.0001278315,0.02213392,0.0005780038,0.4502778],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.09961085,"threshold_uncertainty_score":0.9999233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1951276896160101,"score_gpt":0.3211418086984933,"score_spread":0.1260141190824832,"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."}}