{"id":"W2049657078","doi":"10.1002/hyp.7904","title":"Stormwater quantity and quality response to climate change using artificial neural networks","year":2010,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Calgary","funders":"","keywords":"Environmental science; Stormwater; Downscaling; Surface runoff; Climate change; Precipitation; Hydrology (agriculture); Turbidity; Storm; Water quality; Meteorology; Geography; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008212186,0.0001693392,0.0001750384,0.00003979522,0.0003539015,0.00008261406,0.0002206107,0.0001159696,0.0004590011],"category_scores_gemma":[0.0002745127,0.0001314128,0.00002880281,0.000298329,0.0003286313,0.0003811473,0.0005779271,0.000242621,0.0001306034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003547901,"about_ca_system_score_gemma":0.000003040433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003581301,"about_ca_topic_score_gemma":0.001566823,"domain_scores_codex":[0.9984381,0.0001361021,0.00023968,0.0004659423,0.0002223816,0.0004977626],"domain_scores_gemma":[0.9994012,0.0001059168,0.00006989621,0.0002309388,0.00001292267,0.0001790937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002140217,0.0006102566,0.8904014,0.00008640235,0.00001699018,0.00004567369,0.0009817684,0.01127786,0.09010274,0.001342705,0.0002749091,0.002719085],"study_design_scores_gemma":[0.0003431639,0.0005428237,0.9321573,0.00001019314,0.00005048318,0.0000200875,0.00005142036,0.05802578,0.0008758327,0.001278333,0.005884187,0.0007604254],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966068,0.00001980744,0.001664924,0.0008482831,0.0001818425,0.0003306889,0.000007737581,0.0001218755,0.000218012],"genre_scores_gemma":[0.9978577,0.000005791036,0.0008950313,0.001028095,0.0001280086,0.00004499303,0.000003013165,0.00001128671,0.00002608282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08922691,"threshold_uncertainty_score":0.5358858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1010598464492268,"score_gpt":0.3101007969073739,"score_spread":0.2090409504581471,"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."}}