{"id":"W2041104363","doi":"10.1016/j.jglr.2014.04.004","title":"Re-eutrophication of Lake Erie: Correlations between tributary nutrient loads and phytoplankton biomass","year":2014,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":261,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Department of Natural Resources; U.S. Environmental Protection Agency","keywords":"Phytoplankton; Eutrophication; Tributary; Environmental science; Biomass (ecology); Nutrient; Algal bloom; Phosphorus; Structural basin; Biomanipulation; Abiotic component; Oceanography; Hydrology (agriculture); Ecology; Biology; Geology; Geography; Chemistry","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":[0.0003422533,0.0001715174,0.0003414827,0.0006094031,0.0003629231,0.0006689133,0.0002403574,0.0003171152,0.0009881086],"category_scores_gemma":[0.0009426645,0.0002475537,0.0001930622,0.0006961881,0.0003184455,0.0004491172,0.0007941202,0.0002576018,0.0002033998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006599051,"about_ca_system_score_gemma":0.0006931254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07767446,"about_ca_topic_score_gemma":0.2154015,"domain_scores_codex":[0.9998766,0.0000225467,0.00001476678,0.00002526666,0.00001894278,0.00004176373],"domain_scores_gemma":[0.9996854,0.00006219844,0.00007346549,0.00002690986,0.00009807713,0.00005403822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000518845,0.00004183177,0.9821047,0.00002316597,0.0001113329,0.0002519446,0.0009800405,0.000348316,0.01050481,0.0001043783,0.0001465851,0.004864112],"study_design_scores_gemma":[0.000002880698,0.00001713825,0.9988248,0.000002284329,0.00001608092,0.00003606752,0.0003946864,0.0002613836,0.0002619693,0.00001243298,0.00016787,0.000002437067],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996172,0.00002183592,0.0000151293,0.00001212909,4.963317e-7,6.914734e-7,0.00004319,0.000001700255,0.000287605],"genre_scores_gemma":[0.999401,0.00002442247,0.00004786213,0.00001190538,7.749485e-7,0.000001541415,0.0001021116,0.000002225769,0.0004080652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07767446,"threshold_uncertainty_score":0.1544447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03072342606403511,"score_gpt":0.3013230149050412,"score_spread":0.2705995888410061,"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."}}