{"id":"W2904296272","doi":"10.36939/cjur/vol27no1/art113","title":"Regional Planning and Urban Revitalization in Mid-Sized Cities: A Case Study on Downtown Guelph","year":2018,"lang":"en","type":"article","venue":"Canadian journal of urban research","topic":"Facilities and Workplace Management","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Journal of Administrative Sciences","funders":"","keywords":"Downtown; Plan (archaeology); Incentive; Urban planning; Geography; Investment (military); Economic growth; Comprehensive planning; Environmental planning; Political science; Regional science; Business; Civil engineering; Engineering; Economics; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003027591,0.0001240608,0.0002353858,0.001282531,0.0002990133,0.0001451666,0.0002380295,0.00007966645,0.0009717728],"category_scores_gemma":[0.0002526124,0.0001139078,0.00004109427,0.0005296165,0.0003375891,0.00009604896,0.00003050859,0.000513154,0.0000327882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003334549,"about_ca_system_score_gemma":0.0004234761,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0264195,"about_ca_topic_score_gemma":0.03282909,"domain_scores_codex":[0.9976409,0.000727033,0.000408734,0.000245025,0.0004006219,0.0005777013],"domain_scores_gemma":[0.9984878,0.000325432,0.00008963113,0.0002759718,0.0003157379,0.000505375],"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":[0.0005585337,0.0001316863,0.4353863,0.00004536597,0.0001448682,0.02916384,0.07505004,0.00001611849,0.000008289142,0.002516196,0.4554785,0.001500354],"study_design_scores_gemma":[0.007268976,0.007322631,0.2981009,0.001101415,0.00004016699,0.003504724,0.4506944,0.00006358091,0.000007091577,0.0006777824,0.2307244,0.0004939744],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856962,0.001643902,0.00001385909,0.001450909,0.0003525125,0.0004268114,0.00001150205,0.000004060916,0.01040023],"genre_scores_gemma":[0.9940881,0.000005874792,0.00001647258,0.0001816451,0.0003571709,0.00001474402,0.000002331639,0.00001656728,0.005317139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3756444,"threshold_uncertainty_score":0.9999415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1441181284449709,"score_gpt":0.4002291220714819,"score_spread":0.256110993626511,"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."}}