{"id":"W2792970065","doi":"10.1016/j.jglr.2018.02.003","title":"Hydrological alterations as the major driver on environmental change in a floodplain Lake Poyang (China): Evidence from monitoring and sediment records","year":2018,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Seventh Framework Programme; National Natural Science Foundation of China; National Science Foundation","keywords":"Floodplain; Eutrophication; Environmental science; Benthic zone; Diatom; Hydrology (agriculture); Water quality; Sediment; Plankton; Water level; Nutrient; Lake ecosystem; Ecology; Phytoplankton; Oceanography; Ecosystem; Geology; Geography; Biology","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":[0.0004875682,0.0002272036,0.0001983239,0.001362295,0.0005141094,0.0005846003,0.0003813864,0.000333869,0.0005251079],"category_scores_gemma":[0.0007039644,0.0002207193,0.0002133545,0.00247075,0.0006742059,0.0006721225,0.0007220698,0.0002185946,0.00005334162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006929592,"about_ca_system_score_gemma":0.001018547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07501126,"about_ca_topic_score_gemma":0.1474577,"domain_scores_codex":[0.9998083,0.00003704943,0.00002746021,0.00004924939,0.00003564517,0.00004236563],"domain_scores_gemma":[0.999168,0.0001083916,0.000409947,0.00006158125,0.0001158331,0.0001362104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002529869,0.00001705213,0.9964483,0.00001307599,0.0000391248,0.00007087579,0.0004285827,0.00006802318,0.0008258951,0.0000307791,0.00007097549,0.001962023],"study_design_scores_gemma":[9.173936e-7,0.000005006323,0.9995504,0.000001203318,0.000008240127,0.000008453776,0.0001968581,0.000136941,0.00002611995,0.000009253417,0.00005505152,0.000001559049],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995494,0.00003572057,0.00002493143,0.00003245717,0.000001198271,0.000003332609,0.0001473389,0.000002015259,0.0002034651],"genre_scores_gemma":[0.9996681,0.00004437167,0.00004258523,0.00001060651,0.000003169527,0.000003451439,0.0001600472,7.322442e-7,0.00006693696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07501126,"threshold_uncertainty_score":0.1491493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05437893048555049,"score_gpt":0.3269479463849125,"score_spread":0.272569015899362,"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."}}