{"id":"W4250831536","doi":"10.1016/j.scitotenv.2018.11.237","title":"Mapping landscape-level hydrological connectivity of headwater wetlands to downstream waters: A catchment modeling approach - Part 2","year":2018,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Resources Conservation Service; Global Institute for Water Security, University of Saskatchewan; National Oceanic and Atmospheric Administration; University of Guelph; U.S. Environmental Protection Agency","keywords":"Hydrology (agriculture); Environmental science; Wetland; Groundwater recharge; Streamflow; Watershed; Surface runoff; Evapotranspiration; Soil and Water Assessment Tool; Hydrological modelling; Groundwater; SWAT model; Surface water; Water balance; Drainage basin; Ecology; Geology; Geography; Aquifer; Climatology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002418537,0.0003076779,0.0002910161,0.0008658779,0.0003692278,0.001020494,0.0006087323,0.000451738,0.001499531],"category_scores_gemma":[0.0007150494,0.0002923509,0.0007420335,0.001298735,0.0001840929,0.0006353332,0.0004614725,0.0002084635,0.00009522848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008335726,"about_ca_system_score_gemma":0.001138027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04364587,"about_ca_topic_score_gemma":0.0548071,"domain_scores_codex":[0.9998841,0.00003273421,0.000006108913,0.00004274197,0.00001449932,0.00001975906],"domain_scores_gemma":[0.9998981,0.00005113685,0.00001560804,0.00001164743,0.00001261723,0.00001082639],"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":[0.00004295,0.0001583822,0.08655205,0.0000573885,0.0002383408,0.0001428702,0.0002042796,0.8635272,0.006863103,0.002663746,0.0005717137,0.03897799],"study_design_scores_gemma":[0.00001392433,0.00001459267,0.0465962,0.000006708029,0.00004703932,0.00002529192,0.0001016793,0.9505686,0.0007508869,0.001221321,0.0006421249,0.00001159792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9527208,0.0001245861,0.04320959,0.0001468262,0.000005167318,0.00009055706,0.001154408,0.0001851678,0.002362976],"genre_scores_gemma":[0.9792562,0.0001122379,0.01899674,0.00001165735,0.000004236435,0.00007885482,0.000682445,0.00002438319,0.0008332559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04364587,"threshold_uncertainty_score":0.08678365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02759215845531917,"score_gpt":0.2174152453055875,"score_spread":0.1898230868502683,"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."}}