{"id":"W3167824713","doi":"10.32920/ryerson.14651670.v1","title":"Lessons For Toronto From Melbourne’s City Centre Revitalization","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Downtown; Public space; City centre; Pedestrian; Space (punctuation); Conversation; Sociology; Media studies; Geography; Engineering; Architectural engineering; Archaeology","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":[0.005265833,0.0005890892,0.0005017357,0.0007377066,0.01129684,0.007256729,0.002596739,0.004402428,0.01112493],"category_scores_gemma":[0.008994652,0.000421051,0.0008502719,0.001193516,0.00651339,0.005381206,0.00861743,0.007087974,0.0009406077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06375311,"about_ca_system_score_gemma":0.07490375,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7923175,"about_ca_topic_score_gemma":0.9242173,"domain_scores_codex":[0.993302,0.002800198,0.0001501351,0.000312846,0.0009013037,0.00253352],"domain_scores_gemma":[0.987835,0.001429313,0.0004650368,0.0004506524,0.002104867,0.007715175],"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":[0.0003668962,0.0003431221,0.007589954,0.001449289,0.00009737769,0.004500396,0.169125,0.001234753,0.001386664,0.1572035,0.5633243,0.09337877],"study_design_scores_gemma":[0.00005954364,0.000232386,0.02016409,0.0009602595,0.0000574016,0.0004682134,0.1664166,0.0004785198,0.0005724519,0.01049368,0.7999616,0.0001353664],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1276034,0.01604758,0.001092452,0.7645,0.004215805,0.0002166288,0.0004616402,0.0001444238,0.08571813],"genre_scores_gemma":[0.8510726,0.01116084,0.002513539,0.06479125,0.0009484431,0.0002356405,0.000387181,0.0001881324,0.06870234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2076825,"threshold_uncertainty_score":0.4625633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06547831986399015,"score_gpt":0.3669195389887998,"score_spread":0.3014412191248096,"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."}}