{"id":"W2043222924","doi":"10.1016/j.cageo.2006.02.005","title":"GIS modeling for predicting river runoff volume in ungauged drainages in the Greater Toronto Area, Canada","year":2006,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Surface runoff; Precipitation; Hydrology (agriculture); Drainage basin; Structural basin; Runoff curve number; Environmental science; Streamflow; Drainage; Geology; Meteorology; Geomorphology; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002449945,0.0005993856,0.0003612291,0.0008672642,0.001172081,0.001170138,0.00085722,0.000332793,0.00229585],"category_scores_gemma":[0.001137557,0.0003549592,0.0004087509,0.001503648,0.0003707279,0.0003035931,0.0003117551,0.0003112027,0.0002375972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01959735,"about_ca_system_score_gemma":0.01524518,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9934053,"about_ca_topic_score_gemma":0.9953576,"domain_scores_codex":[0.9998528,0.0000201133,0.000008742549,0.00002854835,0.00004917372,0.00004056815],"domain_scores_gemma":[0.9995497,0.0001098373,0.00002915481,0.00001234,0.0002449465,0.00005393142],"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":[0.0001510495,0.0001398442,0.1591894,0.00008442595,0.00009173742,0.0002571294,0.0003876685,0.813656,0.0009974613,0.00128229,0.006620592,0.01714245],"study_design_scores_gemma":[0.00002521028,0.00002031675,0.05653761,0.00002185389,0.00002330161,0.00002052609,0.0006399197,0.9400818,0.0005329654,0.0002371945,0.001839575,0.00001974096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889392,0.000195478,0.001990508,0.0001693272,0.00001252499,0.00005459982,0.004390882,0.0002655503,0.003981847],"genre_scores_gemma":[0.9916454,0.0002086801,0.002545967,0.00001423029,0.00000303684,0.00002899015,0.001929656,0.00003118131,0.003592795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01959735,"threshold_uncertainty_score":0.1421894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009737135307760995,"score_gpt":0.1943679189953197,"score_spread":0.1846307836875587,"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."}}