{"id":"W2794290614","doi":"10.3390/w10030274","title":"Separating Wet and Dry Years to Improve Calibration of SWAT in Barrett Watershed, Southern California","year":2018,"lang":"en","type":"article","venue":"Water","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Environment; Ministry of the Environment, Conservation and Parks","funders":"","keywords":"Environmental science; Baseflow; Soil and Water Assessment Tool; Hydrology (agriculture); Surface runoff; SWAT model; Watershed; Streamflow; Drainage basin; Geography; Geology; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0007697924,0.0002947387,0.0002338935,0.0004524239,0.0003710846,0.0005834219,0.0005215568,0.0002704178,0.0007177572],"category_scores_gemma":[0.001575311,0.0002381088,0.0002465103,0.0007760536,0.0002190192,0.0006399067,0.0002811141,0.0003351574,0.00007785871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001140597,"about_ca_system_score_gemma":0.001123636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2050212,"about_ca_topic_score_gemma":0.2687998,"domain_scores_codex":[0.9998358,0.00003149911,0.00001576587,0.00006146746,0.00003436948,0.00002112823],"domain_scores_gemma":[0.9996245,0.00009799963,0.00007586319,0.00004830549,0.0001058801,0.00004728623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002171429,0.0003936426,0.5198609,0.0000602137,0.0001555102,0.0001454441,0.0004744045,0.417777,0.007773513,0.000921462,0.003410059,0.04881072],"study_design_scores_gemma":[0.00009526222,0.00005220776,0.367681,0.00002370688,0.00006780675,0.00003078775,0.0002478053,0.6242662,0.003754425,0.0004569535,0.003283683,0.00004017483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934721,0.00005509859,0.004330225,0.00006792823,0.000006932612,0.00002190239,0.0005027616,0.0002311853,0.00131192],"genre_scores_gemma":[0.9944596,0.00005478211,0.004197271,0.00002692876,0.000004312436,0.00001569004,0.000869652,0.00003027578,0.00034156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2050212,"threshold_uncertainty_score":0.4076557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006175952424318984,"score_gpt":0.2093559702238307,"score_spread":0.2031800177995117,"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."}}