{"id":"W4253228958","doi":"10.32920/ryerson.14651670","title":"Lessons For Toronto From Melbourne’s City Centre Revitalization","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Underground infrastructure and sustainability","field":"Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009518107,0.0002724504,0.0003753061,0.00001579013,0.00005697726,0.0001935802,0.0001906355,0.0003948419,0.002362684],"category_scores_gemma":[0.00008470798,0.0002729553,0.000213877,0.00003296216,0.00001541357,0.0002005967,0.0001765395,0.0002090658,0.00000108492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007842848,"about_ca_system_score_gemma":0.0001087285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001933126,"about_ca_topic_score_gemma":0.009505037,"domain_scores_codex":[0.9988732,0.00003071694,0.0003111515,0.0003940491,0.0001318889,0.0002590309],"domain_scores_gemma":[0.9990532,0.00006884748,0.00004420749,0.000545988,0.0002041673,0.0000835608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001358732,0.0005677427,0.01472495,0.01667811,0.005037421,0.0000497604,0.0272839,0.2468968,0.003429021,0.1025451,0.4228599,0.1597914],"study_design_scores_gemma":[0.001966247,0.00005443115,0.03118656,0.0007896185,0.0009082305,0.000004740221,0.01967137,0.21887,0.009898589,0.4612106,0.2511458,0.004293823],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05978798,0.003842106,0.9072775,0.0008879983,0.002366697,0.0009516121,0.0002902711,0.0006643052,0.02393151],"genre_scores_gemma":[0.9819205,0.0003526784,0.0144917,0.00009820807,0.0004176818,0.00004896244,0.0016282,0.00005196896,0.0009901031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9221325,"threshold_uncertainty_score":0.9999723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144923827155315,"score_gpt":0.2529147383779012,"score_spread":0.2384223556623697,"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."}}