{"id":"W4230086703","doi":"10.32920/ryerson.14646996","title":"Making density work: tall buildings in downtown Toronto","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; Toronto Metropolitan University","funders":"","keywords":"Downtown; Realm; Pace; Context (archaeology); Work (physics); Urban planning; Diversity (politics); Environmental planning; Geography; Sociology; Civil engineering; Engineering","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":[0.0003546804,0.0002258384,0.0001365684,0.0005659302,0.008212544,0.002328446,0.0007074178,0.0004787077,0.006225309],"category_scores_gemma":[0.0009303196,0.0001855485,0.0001392069,0.001737968,0.0032603,0.0006650428,0.002318706,0.0007633585,0.0003284678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02940425,"about_ca_system_score_gemma":0.01368643,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9331552,"about_ca_topic_score_gemma":0.9877244,"domain_scores_codex":[0.999321,0.0001892244,0.00001521621,0.00004521119,0.0001205487,0.000308818],"domain_scores_gemma":[0.9988847,0.0001344315,0.0001251507,0.00002982572,0.0001409586,0.0006848757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001218171,0.0001007542,0.1617949,0.000269845,0.0000238797,0.003657572,0.7838655,0.000637477,0.002102961,0.01053574,0.01176836,0.02512132],"study_design_scores_gemma":[0.00000428252,0.00006362928,0.2772713,0.00009868659,0.00001308624,0.0002269132,0.6814537,0.0001489863,0.0001585794,0.0002737509,0.04026938,0.00001765385],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975185,0.0005451402,0.0001649195,0.002155775,0.00003452848,0.00003153372,0.000276549,0.000007297402,0.02159924],"genre_scores_gemma":[0.9909423,0.0006128953,0.0001658698,0.0001250739,0.000009690264,0.00001313089,0.0001326876,0.00000543976,0.007992945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06684482,"threshold_uncertainty_score":0.2133438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05432834557146378,"score_gpt":0.3482904989666977,"score_spread":0.2939621533952339,"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."}}