{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002263277,0.00007086381,0.00009249469,0.00003103932,0.00004973531,0.000009624981,0.00006365778,0.00003755845,0.0004681548],"category_scores_gemma":[0.000006391273,0.00004796896,0.00001195789,0.00004022426,0.0001409773,0.00008246761,0.0002235565,0.00003795821,0.0007608038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002140709,"about_ca_system_score_gemma":7.848556e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005511971,"about_ca_topic_score_gemma":0.0005763524,"domain_scores_codex":[0.9993705,0.00002893091,0.0001313293,0.0001833525,0.00009198246,0.0001939013],"domain_scores_gemma":[0.9998502,0.000004949789,0.00001603623,0.00009407468,0.000003123825,0.0000315875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001166172,0.00004251644,0.8048171,0.0000227287,0.00003333862,0.00001236982,0.03782605,0.0006056967,0.1511983,0.00001080448,0.003003789,0.002310694],"study_design_scores_gemma":[0.003433129,0.001305516,0.1640669,0.00007578842,0.0001155267,0.000004256353,0.00459976,0.02176887,0.7397827,0.005273362,0.05817286,0.001401404],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979279,0.00000278503,0.0001615815,0.0007130193,0.00005095719,0.0001327689,0.00000787726,0.00001116326,0.0009919661],"genre_scores_gemma":[0.9984155,0.000001017379,0.0002883406,0.0004203449,0.00002880118,0.00001053484,0.000004598563,0.000006150519,0.0008247392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6407502,"threshold_uncertainty_score":0.9778847,"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."}}