{"id":"W2531751515","doi":"10.1016/j.scitotenv.2016.09.227","title":"Sediment and nutrient distribution and resuspension in Lake Winnipeg","year":2016,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada","keywords":"Sediment; Tributary; Nutrient; Sedimentation; Hydrology (agriculture); Environmental science; Phosphorus; Watershed; Structural basin; Geology; Geomorphology; Ecology; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008378422,0.00008074628,0.00008595793,0.00001007785,0.00016681,0.00001098657,0.000212799,0.00002102312,0.0001091212],"category_scores_gemma":[0.00002798832,0.00003510996,0.00001727003,0.0001144965,0.001422568,0.0001150694,0.0005934228,0.00004813449,0.0000218124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001508842,"about_ca_system_score_gemma":0.000006683342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006512379,"about_ca_topic_score_gemma":0.00004213523,"domain_scores_codex":[0.998976,0.00004619624,0.0001700127,0.0002237961,0.0003999919,0.0001840134],"domain_scores_gemma":[0.9994977,0.00005062854,0.00008978987,0.0003063295,0.000001347324,0.00005424081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002167327,0.0005851163,0.1059184,0.00005122571,0.00002415527,0.000007146978,0.003585726,0.02505017,0.7943684,0.03142745,0.001035897,0.03772954],"study_design_scores_gemma":[0.0006145272,0.0001243541,0.9696459,0.0001019771,0.00001313906,0.00002945806,0.0001716518,0.008379719,0.01506727,0.004250538,0.00143145,0.000169981],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971668,0.00003078339,0.00008302026,0.001828105,0.00007103431,0.0002515092,0.00002280012,0.000002714556,0.0005432017],"genre_scores_gemma":[0.999464,0.00006045124,0.0000458431,0.00001137964,0.000006942546,0.000006177499,4.61728e-7,0.00000275236,0.0004019344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8637275,"threshold_uncertainty_score":0.5241511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005303904375147111,"score_gpt":0.1845674882403235,"score_spread":0.1792635838651764,"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."}}