{"id":"W3185985347","doi":"10.1002/lom3.10443","title":"A Bayesian mixing model framework for quantifying temporal variation in source of sediment to lakes across broad hydrological gradients of floodplains","year":2021,"lang":"en","type":"article","venue":"Limnology and Oceanography Methods","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Fisheries and Oceans Canada; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Environment and Parks; Suncor Energy Incorporated; BC Hydro; Polar Knowledge Canada; Natural Resources Canada; Canadian Natural Resources Limited","keywords":"Sediment; Floodplain; Hydrology (agriculture); Environmental science; Flood myth; Sampling (signal processing); Range (aeronautics); Physical geography; Geology; Ecology; Geomorphology; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006709254,0.0009656501,0.0007805177,0.00237194,0.0007635539,0.001594722,0.001754872,0.001390134,0.001340327],"category_scores_gemma":[0.007798347,0.0008785734,0.001275213,0.0008659682,0.00128885,0.001426085,0.001687984,0.001243386,0.0002416902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001442334,"about_ca_system_score_gemma":0.001165244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01602937,"about_ca_topic_score_gemma":0.01092713,"domain_scores_codex":[0.9988843,0.000584909,0.00005428208,0.0002592379,0.0001272652,0.00009001148],"domain_scores_gemma":[0.9971342,0.001986214,0.0004246327,0.000104094,0.0002320319,0.0001188432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001230398,0.00007059998,0.008518287,0.00004065297,0.0002068213,0.00007935907,0.0001605401,0.9445162,0.002246354,0.02520693,0.0003709211,0.01846031],"study_design_scores_gemma":[0.00000385699,0.000007516407,0.0005705266,0.000002791101,0.000006263797,0.000004054352,0.000007366866,0.9956612,0.00007371009,0.003584287,0.00007201029,0.000006334805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1063478,0.0001657599,0.8917928,0.0002924098,0.00001799825,0.00006691715,0.0002413606,0.0002876953,0.0007873432],"genre_scores_gemma":[0.8754016,0.0001846514,0.1211867,0.0001010032,0.00005318436,0.0002679758,0.0005822686,0.00009684119,0.002125834],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01602937,"threshold_uncertainty_score":0.03548235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03745960278719817,"score_gpt":0.3456790840246453,"score_spread":0.3082194812374471,"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."}}