{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008113232,0.0002825478,0.000421335,0.00009807337,0.00006408135,0.000064838,0.0003203874,0.0001362981,0.00005892765],"category_scores_gemma":[0.00001364384,0.0002658705,0.00002210276,0.00003467954,0.0002222143,0.00008362634,0.0031432,0.0003222029,0.000001641396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001036607,"about_ca_system_score_gemma":0.0001416512,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8274568,"about_ca_topic_score_gemma":0.9731311,"domain_scores_codex":[0.9990819,0.00001416018,0.000234991,0.000301753,0.00009742546,0.0002697764],"domain_scores_gemma":[0.9995415,0.00007097576,0.00003032048,0.0003099935,0.000007098568,0.00004013565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005328907,0.00004938546,0.2621839,0.001547762,0.002191638,0.0130004,0.006302219,0.00269429,0.00003921003,0.001173104,0.6923696,0.01839522],"study_design_scores_gemma":[0.00361542,0.0003618753,0.08773997,0.002643049,0.0003731867,0.003801467,0.7334812,0.01622946,0.01091805,0.02293918,0.1104311,0.007466011],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774562,0.005162559,0.000006386259,0.0001131691,0.0006752999,0.0004212806,0.0002503148,0.0001493047,0.01576546],"genre_scores_gemma":[0.9940164,0.004397126,0.0004044811,0.00005834771,0.00004175562,0.000131391,0.00002365349,0.00003083045,0.0008959788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7271791,"threshold_uncertainty_score":0.9999794,"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."}}