{"id":"W2068589359","doi":"10.1111/j.1752-1688.2002.tb04350.x","title":"MODELING THE HYDROCHEMISTRY OF THE CANNONSVILLE WATERSHED WITH GENERALIZED WATERSHED LOADING FUNCTIONS (GWLF)<sup>1</sup>","year":2002,"lang":"en","type":"article","venue":"JAWRA Journal of the American Water Resources Association","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"Smiths Detection (Canada)","funders":"U.S. Geological Survey","keywords":"Watershed; Sediment; Environmental science; Hydrology (agriculture); Streamflow; Nutrient; Particulates; Geology; Drainage basin; Geotechnical engineering; Ecology; Geomorphology","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":[0.000775773,0.0001896335,0.0003198741,0.00004309929,0.0007164057,0.0000478059,0.0006874588,0.00004961618,0.0001842056],"category_scores_gemma":[0.00005540564,0.00007287239,0.000227895,0.0003019643,0.0003588001,0.0001628848,0.0003437475,0.000348501,0.00002341243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003293079,"about_ca_system_score_gemma":0.000003445297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006325945,"about_ca_topic_score_gemma":0.00004724888,"domain_scores_codex":[0.997969,0.0003582244,0.0004555135,0.0001823982,0.0006412602,0.0003935674],"domain_scores_gemma":[0.9988053,0.00005719318,0.0007070042,0.0003398557,0.00004337868,0.0000472602],"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":[0.0001548865,0.00008258095,0.1899593,0.0000101937,0.0005774881,0.000003132139,0.0194698,0.7690271,0.01487029,0.000001050867,0.005620604,0.000223596],"study_design_scores_gemma":[0.009109953,0.001362692,0.06240132,0.0003996586,0.004166684,0.000315384,0.03255426,0.6465611,0.1384702,0.001107326,0.1013385,0.002212975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853063,0.00003124658,0.0001280706,0.01331761,0.00007595265,0.000158535,0.000003470133,0.00001153383,0.0009673084],"genre_scores_gemma":[0.9954293,0.00003262146,0.00007222706,0.0006866535,0.0001040153,0.000007676917,0.000001419875,0.00001710191,0.003649001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1275579,"threshold_uncertainty_score":0.5510086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007789436302949303,"score_gpt":0.1858215491069052,"score_spread":0.1780321128039558,"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."}}