{"id":"W2085945193","doi":"10.2166/nh.2009.084","title":"Impacts of acid deposition at Plastic Lake: forecasting chemical recovery using a Bayesian calibration and uncertainty propagation approach","year":2009,"lang":"en","type":"article","venue":"Hydrology research","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Environmental science; Calibration; Acid deposition; Hydrology (agriculture); Uncertainty analysis; Groundwater; Soil science; Soil water; Statistics; Geology; Mathematics","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":[0.0004917316,0.00008164396,0.0001162615,0.00007627848,0.0001586339,0.00001809148,0.00008152345,0.0001282663,0.00001654257],"category_scores_gemma":[0.000140716,0.00007148353,0.00002113346,0.000252175,0.0002575053,0.0001997648,0.0001164922,0.0001754981,0.000002193862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001684937,"about_ca_system_score_gemma":0.00001468553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001153753,"about_ca_topic_score_gemma":0.00005595045,"domain_scores_codex":[0.9987886,0.0001571917,0.0001762784,0.0002680669,0.0002852629,0.0003245862],"domain_scores_gemma":[0.9996474,0.00008191112,0.0000578233,0.0001101402,0.00001575776,0.00008698065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008848665,0.0002136777,0.5527803,0.00005927165,0.00001359095,0.00001641897,0.000758371,0.03371842,0.4061007,0.00004567715,0.00006581479,0.005342898],"study_design_scores_gemma":[0.0003099897,0.0002985751,0.006849159,0.00001475536,0.000007429856,0.00008096929,0.00001549925,0.9759656,0.01333057,0.003051515,0.000001974464,0.00007397803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910135,0.00001437027,0.008298692,0.0001084,0.00001844606,0.0002042999,0.000004285604,0.00001363866,0.0003243547],"genre_scores_gemma":[0.9985431,0.000006857319,0.001312442,0.00002478638,0.00002783493,0.000006934312,0.00006361599,0.000006433069,0.000007981748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9422472,"threshold_uncertainty_score":0.2915013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03659229920601768,"score_gpt":0.2778895814398364,"score_spread":0.2412972822338187,"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."}}