{"id":"W1824092588","doi":"10.1029/2012wr011821","title":"A Bayesian methodological framework for accommodating interannual variability of nutrient loading with the SPARROW model","year":2012,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of the Environment, Conservation and Parks; University of Toronto","funders":"","keywords":"Environmental science; Watershed; Sparrow; Hydrology (agriculture); Bayesian inference; Bayesian probability; Statistics; Computer science; Ecology; Mathematics; Geology","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":[0.0120036,0.0007623092,0.0008785294,0.001137748,0.000634774,0.001507352,0.002722114,0.001135903,0.001649347],"category_scores_gemma":[0.02286239,0.001013966,0.001020688,0.0009879547,0.001321599,0.00214377,0.00145809,0.001769897,0.0002516993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0015506,"about_ca_system_score_gemma":0.002833924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02223377,"about_ca_topic_score_gemma":0.02335775,"domain_scores_codex":[0.997501,0.001564232,0.000110513,0.0004409321,0.0002915176,0.0000918606],"domain_scores_gemma":[0.9899952,0.007558962,0.001002826,0.0006228575,0.0006805132,0.0001397041],"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":[0.00002336226,0.00003050878,0.002629844,0.00004730942,0.0001318744,0.0001202076,0.0002301394,0.7237408,0.0006674616,0.2435024,0.0005862722,0.02828983],"study_design_scores_gemma":[0.00001269262,0.00002055698,0.0004581534,0.00001526686,0.00002554247,0.00003378475,0.00002748001,0.9087673,0.0001513902,0.08869633,0.001771133,0.00002030758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002625113,0.00005161027,0.9967571,0.0001192273,0.000007523524,0.00001076054,0.00003197234,0.00003574137,0.0003609606],"genre_scores_gemma":[0.2345831,0.0004564619,0.7611917,0.0002323697,0.0001251352,0.0004216969,0.0002463153,0.0001112318,0.002632009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02223377,"threshold_uncertainty_score":0.06348187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1276681114189107,"score_gpt":0.3774819397929762,"score_spread":0.2498138283740655,"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."}}