{"id":"W1983428557","doi":"10.1016/j.jglr.2011.10.002","title":"Use of flow-normalization to evaluate nutrient concentration and flux changes in Lake Champlain tributaries, 1990–2009","year":2011,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"New York State Department of Environmental Conservation","keywords":"Tributary; Environmental science; Hydrology (agriculture); Streamflow; Flux (metallurgy); Population; Normalization (sociology); Geography; Drainage basin; Geology; Chemistry; Demography","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":[0.0006452648,0.000227219,0.0002799648,0.001217616,0.0007417526,0.0006494633,0.0005264425,0.0003107536,0.000575408],"category_scores_gemma":[0.001653533,0.0001888841,0.0002382101,0.001643697,0.0003619335,0.0006635145,0.0005000301,0.0002092385,0.000169253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002498673,"about_ca_system_score_gemma":0.001868908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4314285,"about_ca_topic_score_gemma":0.5852255,"domain_scores_codex":[0.9997481,0.00005585483,0.00002734363,0.00006788991,0.00006796498,0.00003292099],"domain_scores_gemma":[0.9992545,0.0001290014,0.0001592174,0.00004470561,0.0003470229,0.00006546536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002703693,0.00008102582,0.9726847,0.00002716583,0.0000636576,0.00007933627,0.0006241067,0.002573098,0.004098046,0.000147814,0.0007567556,0.01859397],"study_design_scores_gemma":[0.00001019174,0.00001356383,0.9912322,0.000004607861,0.00001471069,0.00002178372,0.000177911,0.007332093,0.0006458762,0.00003880862,0.000500654,0.000007436864],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980135,0.00003945847,0.0003491937,0.0000502504,0.000003859222,0.00001059368,0.0004982149,0.00005619492,0.0009787329],"genre_scores_gemma":[0.9967749,0.0000250138,0.00155279,0.00001646223,0.000003463179,0.00002578742,0.0009384389,0.00001194198,0.0006511874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4314285,"threshold_uncertainty_score":0.8578346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08734184385826253,"score_gpt":0.3084099456491308,"score_spread":0.2210681017908682,"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."}}