{"id":"W2014962160","doi":"10.1016/j.scitotenv.2014.08.030","title":"Evaluating rain gardens as a method to reduce the impact of sewer overflows in sources of drinking water","year":2014,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":103,"is_retracted":false,"has_abstract":false,"ca_institutions":"Safe Engineering Services & Technologies (Canada); Natural Sciences and Engineering Research Council of Canada; Institut National de la Recherche Scientifique; Polytechnique Montréal","funders":"Canada Foundation for Innovation","keywords":"Combined sewer; Environmental science; Impervious surface; Stormwater; Drainage; Surface runoff; Hydrology (agriculture); Drainage basin; Low-impact development; Precipitation; Environmental engineering; Water resource management; Stormwater management; Engineering; Geography; Meteorology; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005695442,0.0001480097,0.0001881394,0.00005793042,0.0002634106,0.00001840648,0.001366602,0.00002325058,0.0009298743],"category_scores_gemma":[0.0001335368,0.0000632205,0.0001480975,0.0003215902,0.001413558,0.0001126261,0.001906441,0.0001248245,0.00009614138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003325907,"about_ca_system_score_gemma":0.00001483281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001378172,"about_ca_topic_score_gemma":0.000009951229,"domain_scores_codex":[0.9974431,0.0003745092,0.0003674499,0.0003160527,0.001089167,0.0004096618],"domain_scores_gemma":[0.9987075,0.0001247752,0.000200577,0.0009057202,0.000005382004,0.00005603881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00001323853,0.00004743238,0.0008990638,0.000001898041,0.000009237955,6.442226e-8,0.004340473,0.5555013,0.438249,0.00003181154,0.00005290695,0.0008535554],"study_design_scores_gemma":[0.0004715153,0.000520033,0.481019,0.00006809302,0.00007740075,0.00001135185,0.0009390248,0.06361454,0.4493879,0.003470813,0.0001558322,0.0002644082],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993226,0.000006979795,0.0003039488,0.0008341607,0.00005552566,0.0005299926,0.000001890958,0.000004558818,0.005036929],"genre_scores_gemma":[0.9960356,0.000001304002,0.002153168,0.00002769421,0.00001368007,0.0000179428,1.680751e-7,0.000009125917,0.001741264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4918868,"threshold_uncertainty_score":0.9999834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02257938957147918,"score_gpt":0.297691757383382,"score_spread":0.2751123678119028,"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."}}