{"id":"W2797878944","doi":"10.31235/osf.io/e4fs8","title":"Open Smart Cities in Canada: Environmental Scan and Case Studies - Executive Summary","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Smart city; Open government; Government (linguistics); Business; Executive summary; Executive director; Open data; Public relations; Engineering; Political science; Management; Finance; Economics; 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":[0.002501829,0.0004644385,0.0005202325,0.005295755,0.01127527,0.006511675,0.001489834,0.001173163,0.008962309],"category_scores_gemma":[0.005878934,0.0003887009,0.0005183023,0.02315185,0.003408806,0.001904321,0.002511637,0.001196901,0.0007578535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09129942,"about_ca_system_score_gemma":0.1377396,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9924231,"about_ca_topic_score_gemma":0.9975162,"domain_scores_codex":[0.9960378,0.0003238384,0.0001034254,0.0001934076,0.002411724,0.0009297715],"domain_scores_gemma":[0.9917394,0.001999754,0.0003571356,0.0002444076,0.005032391,0.0006269318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003098316,0.0004241642,0.1794283,0.003669411,0.0001036402,0.01003956,0.1177802,0.007280145,0.002095171,0.07942914,0.3278305,0.27161],"study_design_scores_gemma":[0.00001935497,0.00007231216,0.1264105,0.001860188,0.00005819845,0.0009419405,0.1707643,0.001082892,0.001186309,0.002397553,0.6950666,0.0001399199],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5391434,0.03111535,0.002121752,0.02167191,0.000334494,0.001068463,0.01820441,0.0002284932,0.3861117],"genre_scores_gemma":[0.8497534,0.04167258,0.007077692,0.002943366,0.0001186439,0.0004364161,0.009257437,0.0002429626,0.08849759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09129942,"threshold_uncertainty_score":0.6624268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02415798930273262,"score_gpt":0.2268555113704256,"score_spread":0.2026975220676929,"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."}}