{"id":"W4417178424","doi":"10.1016/j.jhydrol.2025.134776","title":"A cell-averaged numerical model for flow in horizontal subsurface flow wetlands","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Constructed Wetlands for Wastewater Treatment","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Auburn University; U.S. Environmental Protection Agency","keywords":"Subsurface flow; Flow (mathematics); Monte Carlo method; Hydraulic conductivity; Hydrology (agriculture); Yield (engineering); Calibration; Sensitivity (control systems); Computer simulation; Wetland","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0003041444,0.0004523424,0.0008932571,0.0003394425,0.0009121847,0.001055981,0.001538987,0.001539044,0.001396827],"category_scores_gemma":[0.001173128,0.0003994152,0.0006230088,0.0006224897,0.0008576898,0.0007663742,0.0007156862,0.0009017444,0.0002078236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001376472,"about_ca_system_score_gemma":0.002071723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05853999,"about_ca_topic_score_gemma":0.02453134,"domain_scores_codex":[0.9998381,0.00003978545,0.00001207247,0.00003439835,0.00004264506,0.00003302024],"domain_scores_gemma":[0.9994959,0.0002022378,0.00004863303,0.00003397274,0.000144167,0.00007511579],"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":[0.00001394549,0.00003002867,0.0005908054,0.000009980991,0.000007895916,0.0000294743,0.0000193546,0.9939716,0.001253663,0.002082908,0.0001881279,0.001802052],"study_design_scores_gemma":[0.000003494174,0.000004075981,0.00006910357,7.370882e-7,0.000001351493,0.000002097287,0.000002682507,0.9995285,0.00009224118,0.0002035638,0.00008972231,0.000002458374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5315732,0.000614021,0.4489442,0.0006775829,0.0003207056,0.0001697071,0.001177213,0.0008008894,0.01572246],"genre_scores_gemma":[0.9618848,0.0002449173,0.03262424,0.00007827394,0.000069535,0.0001837405,0.0003882677,0.00009330919,0.004432959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05853999,"threshold_uncertainty_score":0.1163985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005858076652203354,"score_gpt":0.2207938193070401,"score_spread":0.2149357426548367,"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."}}