{"id":"W1507144122","doi":"10.1002/hyp.10290","title":"Hydrological footprints of urban developments in the Lake Simcoe watershed, Canada: a combined paired‐catchment and change detection modelling approach","year":2014,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"U.S. Geological Survey; Sveriges Lantbruksuniversitet; Trent University; Stiftelsen för Miljöstrategisk Forskning","keywords":"Watershed; Hydrology (agriculture); Drainage basin; Environmental science; Evapotranspiration; Precipitation; Urbanization; Land use, land-use change and forestry; Land use; Hydrological modelling; Population; Baseflow; Climate change; Physical geography; Geography; Streamflow; Climatology; Geology; Meteorology; Ecology; 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":[0.0002163098,0.0004118881,0.0003003525,0.0008032122,0.0008701742,0.0009514132,0.0009164019,0.0005076344,0.001690389],"category_scores_gemma":[0.0006346342,0.0003027297,0.000600057,0.001548782,0.00050674,0.0003197293,0.0006523277,0.00031673,0.0001065994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0124699,"about_ca_system_score_gemma":0.01000947,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.977721,"about_ca_topic_score_gemma":0.9801291,"domain_scores_codex":[0.9998871,0.00001515164,0.000004153667,0.00003845845,0.00002425761,0.00003083217],"domain_scores_gemma":[0.9997529,0.00004136091,0.00002556733,0.00001504397,0.0001126728,0.00005246246],"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":[0.000285289,0.0002871631,0.3718346,0.00009812786,0.0003612107,0.0003107924,0.0006274953,0.5988731,0.002369156,0.001942479,0.002368174,0.0206424],"study_design_scores_gemma":[0.00005681214,0.00003960986,0.2403743,0.00001714823,0.00008164783,0.00002960463,0.0008290549,0.7561367,0.0004344651,0.0003914511,0.001560744,0.00004843235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950536,0.00005246604,0.001171453,0.00008598798,0.000002932313,0.00004691521,0.001969967,0.00005033935,0.001566383],"genre_scores_gemma":[0.9964823,0.00004161759,0.001378998,0.00001402203,0.000001528439,0.00003133824,0.0011768,0.00000891659,0.0008645787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02227896,"threshold_uncertainty_score":0.09047592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03192267362894207,"score_gpt":0.1997775887570172,"score_spread":0.1678549151280752,"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."}}