{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007490131,0.0001473617,0.0002233388,0.00005550798,0.0003741835,0.00006166013,0.0006623088,0.0001178497,0.0001033627],"category_scores_gemma":[0.0003790206,0.00006783346,0.00007945277,0.0002554397,0.0006555204,0.0001901232,0.0007579701,0.0005055243,0.00002090373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001182537,"about_ca_system_score_gemma":0.000004638403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001154958,"about_ca_topic_score_gemma":0.000004245024,"domain_scores_codex":[0.9969298,0.0008783133,0.0002520994,0.0003382377,0.0007067123,0.0008948756],"domain_scores_gemma":[0.9977701,0.001498175,0.00005309962,0.0004739193,0.00004307,0.000161691],"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":[0.001396633,0.0005946713,0.906331,0.0001236119,0.00006454661,0.000002403607,0.06534749,0.01510359,0.003133707,0.003496348,0.0004499243,0.003956094],"study_design_scores_gemma":[0.001749119,0.001260987,0.01397609,0.0002228728,0.00007924154,0.00003478529,0.007226602,0.5263693,0.05649782,0.3686489,0.02301522,0.000919067],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7479293,0.000009944882,0.249901,0.0008026392,0.00002490483,0.0004395793,0.000009017564,0.00001798676,0.0008655726],"genre_scores_gemma":[0.9551788,0.000001945535,0.04437624,0.0000613813,0.00008659106,0.0001405354,0.000006370325,0.00001944093,0.0001286761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8923549,"threshold_uncertainty_score":0.2877955,"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."}}