{"id":"W4247582254","doi":"10.32920/ryerson.14654640","title":"Unbalanced Growth in Downtown Toronto: Maintaining Employment Uses in Toronto’s Downtown Core","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Downtown; Pace; Investment (military); Population growth; Happening; Economic geography; Economic growth; Demographic economics; Population; Geography; Business; Development economics; Economics; Political science; Sociology; History; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001649397,0.0001772504,0.0001300638,0.0007652887,0.005023888,0.001961379,0.0005359289,0.0002795607,0.004688965],"category_scores_gemma":[0.0005866572,0.0001355273,0.0001351955,0.002623072,0.00139944,0.0006630265,0.001636387,0.0004386053,0.0002976373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02888334,"about_ca_system_score_gemma":0.01468655,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9677948,"about_ca_topic_score_gemma":0.9939162,"domain_scores_codex":[0.9997277,0.00002852894,0.000007295809,0.0000286454,0.00006154005,0.0001464026],"domain_scores_gemma":[0.9994507,0.00003256447,0.00008887593,0.00002060438,0.0001434769,0.0002637766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000188582,0.00007954577,0.7064182,0.0002104374,0.00005106586,0.004677562,0.1910856,0.001189863,0.003208354,0.01141299,0.03107764,0.05040037],"study_design_scores_gemma":[0.00000361078,0.00002966598,0.832473,0.00005039928,0.00001388037,0.0001819917,0.1293776,0.0003697026,0.0002789609,0.0002197377,0.03698566,0.00001562794],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984153,0.0003075385,0.00008394632,0.0009722285,0.00001516273,0.00001204717,0.0005996733,0.000006942719,0.01384951],"genre_scores_gemma":[0.9925856,0.0003138772,0.0001126156,0.00007483666,0.000004705307,0.000007327033,0.0003597418,0.00000527366,0.006535953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03220522,"threshold_uncertainty_score":0.2095643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05174313719479254,"score_gpt":0.3532551422300976,"score_spread":0.3015120050353051,"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."}}