{"id":"W1922328182","doi":"10.2166/wst.2015.368","title":"An event-based hydrologic simulation model for bioretention systems","year":2015,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Wisconsin Department of Natural Resources; U.S. Department of Agriculture","keywords":"Bioretention; Stormwater; Environmental science; Evapotranspiration; Hydrology (agriculture); Storm; Drainage; Infiltration (HVAC); Soil water; Low-impact development; Environmental engineering; Surface runoff; Soil science; Engineering; Geotechnical engineering; Stormwater management; Meteorology; Geography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035963,0.0006175437,0.0006215012,0.0003770776,0.0005971996,0.0009552175,0.001282954,0.001415743,0.002762505],"category_scores_gemma":[0.0009686715,0.0004024303,0.0006971784,0.0004868534,0.0004848852,0.0007530908,0.0007120669,0.001050065,0.0002518424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001424163,"about_ca_system_score_gemma":0.001430536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03636026,"about_ca_topic_score_gemma":0.01991993,"domain_scores_codex":[0.9998597,0.00004019384,0.00001302767,0.00004003691,0.00002155146,0.0000254343],"domain_scores_gemma":[0.9996433,0.0001763049,0.00004225746,0.00002402711,0.00006994131,0.00004413613],"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":[0.00001848313,0.00001527741,0.0005732812,0.000005579722,0.000004843976,0.00002026787,0.00001094636,0.9979242,0.0002597681,0.0005837556,0.00008769956,0.0004958204],"study_design_scores_gemma":[0.000006525446,0.000004575065,0.00009515938,8.006929e-7,0.000001696325,0.000002057735,0.000003972104,0.9994909,0.00008324102,0.0001826284,0.0001264181,0.000002059025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6799774,0.0002627386,0.2937329,0.0007883006,0.0001378437,0.0003786169,0.004939694,0.001636933,0.01814562],"genre_scores_gemma":[0.9726886,0.0001527611,0.0208457,0.00004505197,0.00002057378,0.0002742329,0.001278359,0.00006175382,0.004632984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03636026,"threshold_uncertainty_score":0.07229728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03906466627127564,"score_gpt":0.278798392248332,"score_spread":0.2397337259770563,"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."}}