{"id":"W2317379274","doi":"10.14796/jwmm.r246-15","title":"Shades of Green: Using SWMM LID Controls to Simulate Green Infrastructure","year":2013,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Green infrastructure; Storm Water Management Model; Low-impact development; Stormwater management; Environmental planning; Business; Urban infrastructure; Storm; Urban planning; Stormwater; Civil engineering; Engineering; Environmental science; Geography; Meteorology; Surface runoff; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004296298,0.0002300619,0.0003533811,0.0003056592,0.000120664,0.00007899471,0.0005992734,0.0000550735,0.001050249],"category_scores_gemma":[0.000005801653,0.0001648626,0.0001638209,0.0002025445,0.00005362552,0.0009394359,0.0007296666,0.0001726963,0.0001699334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002380095,"about_ca_system_score_gemma":0.00000345232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009992188,"about_ca_topic_score_gemma":0.00002108709,"domain_scores_codex":[0.997746,0.00005410034,0.0008387697,0.0002461374,0.0006523196,0.0004626641],"domain_scores_gemma":[0.9991701,0.00001111584,0.0002610868,0.0003221058,0.00006378578,0.0001718402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003160897,0.00004643415,0.003374612,0.00004447962,0.0001651986,0.00001838192,0.0009131294,0.9703442,0.02186676,0.00002928137,0.0006741668,0.002491725],"study_design_scores_gemma":[0.001275681,0.0001918894,0.003899608,0.0001392201,0.0003268098,0.00001678173,0.0005201317,0.9808037,0.001307499,0.007738257,0.003365815,0.0004146344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8819429,0.00002219948,0.115111,0.0008017222,0.000167387,0.0005011758,0.000001990278,0.00001795973,0.001433688],"genre_scores_gemma":[0.9768829,0.000008590659,0.02118562,0.0004501653,0.0001008123,0.000005989794,0.000001522069,0.00002847616,0.001335921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09494001,"threshold_uncertainty_score":0.9998629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01683591977664341,"score_gpt":0.2254917606967957,"score_spread":0.2086558409201523,"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."}}