{"id":"W2791888321","doi":"10.3390/w10020222","title":"Hydrological Responses to Various Land Use, Soil and Weather Inputs in Northern Lake Erie Basin in Canada","year":2018,"lang":"en","type":"article","venue":"Water","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Ministry of the Environment, Conservation and Parks; University of Guelph","funders":"","keywords":"Soil and Water Assessment Tool; Environmental science; Watershed; Hydrology (agriculture); Surface runoff; Evapotranspiration; SWAT model; Land use; Drainage basin; Streamflow; Structural basin; Water quality; Geography; Ecology; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002234044,0.000309853,0.0003764347,0.0004253099,0.001166375,0.001239653,0.0006037859,0.0003591192,0.001113052],"category_scores_gemma":[0.0009128642,0.0002343158,0.0004332565,0.001236188,0.0005268995,0.0003056265,0.0005712242,0.0003495034,0.00008456247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0244497,"about_ca_system_score_gemma":0.01557753,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.987977,"about_ca_topic_score_gemma":0.993272,"domain_scores_codex":[0.9997311,0.0000290292,0.00001145432,0.00004727338,0.00007135147,0.0001097471],"domain_scores_gemma":[0.9996064,0.00005422185,0.0000242392,0.00001380436,0.0002174827,0.00008388102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006684072,0.0003750235,0.6790802,0.0001867653,0.0004258043,0.001589524,0.001895092,0.2733368,0.0144238,0.001760892,0.005308358,0.02094934],"study_design_scores_gemma":[0.0001222391,0.00008053305,0.7995674,0.00003319177,0.0001557352,0.0001155312,0.004669273,0.1871273,0.002828953,0.0003243691,0.004871814,0.0001037129],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974777,0.00004674922,0.0001036219,0.00009196487,0.000002546198,0.00001945288,0.0008027177,0.00004393091,0.001411325],"genre_scores_gemma":[0.9977505,0.00009518085,0.0003141062,0.00003369047,8.377767e-7,0.00001207805,0.0008695228,0.00001014895,0.0009140804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0244497,"threshold_uncertainty_score":0.1773958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00955946514897837,"score_gpt":0.1940493245206579,"score_spread":0.1844898593716796,"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."}}