{"id":"W2093030628","doi":"10.1016/j.jglr.2014.06.001","title":"Lagrangian analysis of the transport and processing of agricultural runoff in the lower Maumee River and Maumee Bay","year":2014,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; Great Lakes Protection Fund; U.S. Department of Agriculture; Bowling Green State University; U.S. Department of Commerce; U.S. Environmental Protection Agency","keywords":"Bay; Surface runoff; Hydrology (agriculture); Environmental science; Plume; Deposition (geology); Nitrate; Water column; Storm; Particulates; Water quality; Eutrophication; Nutrient; Sediment; Oceanography; Geology; Geomorphology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002099848,0.0003521877,0.0003266117,0.0007083981,0.0007346425,0.001214658,0.000704208,0.0007383561,0.001335368],"category_scores_gemma":[0.0008131372,0.0003665429,0.0005740648,0.0005821047,0.0004483028,0.000454018,0.0004860079,0.0003207869,0.0001352419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002025171,"about_ca_system_score_gemma":0.001590166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1592479,"about_ca_topic_score_gemma":0.1157975,"domain_scores_codex":[0.9999242,0.0000128024,0.000004996945,0.0000147027,0.00001219727,0.00003111666],"domain_scores_gemma":[0.9998103,0.00005937796,0.00004109956,0.00001398561,0.00004153948,0.00003365202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003243817,0.0003243517,0.1396984,0.00003957378,0.0002070175,0.0005768614,0.0003359232,0.8300914,0.0165264,0.002970045,0.0005880435,0.00831749],"study_design_scores_gemma":[0.00004589386,0.00003880409,0.06442595,0.000004427779,0.00002621033,0.00001694737,0.0001852882,0.9340954,0.0007489367,0.0001850811,0.0002039594,0.00002304387],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981066,0.00002238865,0.0009162346,0.00009928081,0.000004301418,0.000004774545,0.00008696445,0.00002006581,0.0007395394],"genre_scores_gemma":[0.9989366,0.00001704503,0.0004947939,0.00000987669,0.000002675835,0.000003758943,0.0001013121,0.000009085999,0.000424903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1592479,"threshold_uncertainty_score":0.3166419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0157611000458646,"score_gpt":0.2628671417062809,"score_spread":0.2471060416604163,"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."}}