{"id":"W4410357761","doi":"10.1080/09640568.2025.2494752","title":"The community benefits of choosing grey over green infrastructure in planning the Rockcliffe Riverine Flood Mitigation Project in Toronto","year":2025,"lang":"en","type":"article","venue":"Journal of Environmental Planning and Management","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Green infrastructure; Flood mitigation; Flood myth; Environmental planning; Water infrastructure; Infrastructure planning; Business; Environmental resource management; Water resource management; Geography; Environmental science; Civil engineering; Engineering; Environmental engineering; Archaeology; Water supply","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":[0.004276287,0.0002402563,0.0001131396,0.0006192676,0.01074977,0.003188119,0.0006804995,0.0006819376,0.0034454],"category_scores_gemma":[0.006482496,0.000241118,0.0001300345,0.001005239,0.006948608,0.001181558,0.003433363,0.0008994375,0.0001436619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04000101,"about_ca_system_score_gemma":0.04761161,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.662424,"about_ca_topic_score_gemma":0.923364,"domain_scores_codex":[0.9956496,0.002691695,0.00006465309,0.0001388429,0.0004487604,0.001006317],"domain_scores_gemma":[0.9953883,0.002548579,0.0002933621,0.000122006,0.0005488713,0.001098868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002269734,0.0002730968,0.04964862,0.0004677926,0.00003166189,0.003633013,0.7503064,0.01054482,0.005544935,0.05095357,0.01186907,0.1165001],"study_design_scores_gemma":[0.00001440697,0.0001541963,0.03221153,0.0001907597,0.00001697664,0.00009472883,0.92102,0.001588459,0.001109396,0.00345101,0.04010779,0.00004071385],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9581966,0.0001468144,0.001359168,0.004486372,0.000008611087,0.000192023,0.00005731647,0.0000150971,0.03553803],"genre_scores_gemma":[0.9960256,0.0001400165,0.001400672,0.0001099318,0.000001459384,0.00004841023,0.00002025997,0.000005808498,0.00224789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.337576,"threshold_uncertainty_score":0.6791281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00836832701523371,"score_gpt":0.2427403721793022,"score_spread":0.2343720451640685,"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."}}