{"id":"W4385549498","doi":"10.1061/9780784485002.001","title":"Getting Community Support for Large-Scale Urban Flood Mitigation Improvements: The Blounts Creek Watershed Story","year":2023,"lang":"en","type":"article","venue":"","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Works and Government Services Canada","funders":"","keywords":"Watershed; Downtown; Flood myth; Plan (archaeology); Work (physics); Bridge (graph theory); Environmental planning; Watershed management; Scale (ratio); Environmental resource management; Civil engineering; Business; Engineering; Environmental science; Computer science; Geography; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.004927654,0.0005559482,0.0003585079,0.0005900377,0.01675355,0.007269189,0.002348554,0.008513682,0.01914805],"category_scores_gemma":[0.007676681,0.0004075743,0.0005656441,0.0008155972,0.004342774,0.005603244,0.01088343,0.009277089,0.001764156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003751206,"about_ca_system_score_gemma":0.02235729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04816127,"about_ca_topic_score_gemma":0.1844201,"domain_scores_codex":[0.9948964,0.002306832,0.00005800666,0.0001959352,0.0008438588,0.001698976],"domain_scores_gemma":[0.989446,0.001698547,0.0002269478,0.0002667425,0.0009426508,0.007419152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00008991972,0.001051684,0.005027679,0.0001736564,0.00003462775,0.002154244,0.02903258,0.0002408971,0.001055272,0.0218163,0.8180823,0.1212408],"study_design_scores_gemma":[0.0001283436,0.0003593239,0.008661601,0.000489302,0.00002912153,0.0006341112,0.1050634,0.0003972961,0.0004590997,0.009329707,0.8743675,0.00008108089],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.06962331,0.00168846,0.0008505629,0.8503752,0.002162898,0.0001414082,0.0001281873,0.0002298405,0.07480019],"genre_scores_gemma":[0.5645224,0.005934959,0.005667675,0.3047124,0.001784055,0.000635176,0.000538378,0.0004824415,0.1157225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04816127,"threshold_uncertainty_score":0.0957619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01743511320311217,"score_gpt":0.2363460168980187,"score_spread":0.2189109036949065,"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."}}