{"id":"W4413533508","doi":"10.64628/aam.evaa9v9yt","title":"Revitalizing Toronto’s downtown core after COVID-19 greatly benefits the city and the region","year":2024,"lang":"en","type":"article","venue":"","topic":"Impact of Education Environments","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Downtown; Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Core (optical fiber); Geography; Medicine; Virology; Engineering; Archaeology; Telecommunications; Outbreak","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":[0.0008702223,0.0007303384,0.0004556936,0.0006461707,0.01778327,0.007635956,0.001245392,0.004799258,0.0754968],"category_scores_gemma":[0.002599258,0.000426057,0.0007017119,0.0008135964,0.003855878,0.001489779,0.005864646,0.006094988,0.007988527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04993743,"about_ca_system_score_gemma":0.1259157,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9248978,"about_ca_topic_score_gemma":0.9894191,"domain_scores_codex":[0.9979664,0.0001321737,0.00002591344,0.0001125769,0.0003973406,0.00136554],"domain_scores_gemma":[0.9939871,0.0001340196,0.00009598477,0.0001460627,0.0009107268,0.004726136],"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":[0.0001561274,0.00008571678,0.00815887,0.0001931864,0.00003159292,0.001725973,0.006803271,0.0004722856,0.001561493,0.05493981,0.8904895,0.0353822],"study_design_scores_gemma":[0.00001340392,0.00002924275,0.01380974,0.00006467582,0.000008598632,0.00005735678,0.005482685,0.00006701213,0.0002308663,0.0006043799,0.9796084,0.00002371638],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.09547434,0.006227428,0.002852384,0.2594302,0.02074,0.0004523312,0.003536465,0.001111647,0.6101752],"genre_scores_gemma":[0.1738738,0.001226895,0.001464516,0.02202783,0.0006292175,0.00009807443,0.000803818,0.0003112403,0.7995646],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.0754968,"threshold_uncertainty_score":0.3623231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07943101539473497,"score_gpt":0.3663751690651258,"score_spread":0.2869441536703909,"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."}}