{"id":"W2062234814","doi":"10.1007/s11242-004-6325-z","title":"Modeling Floodplain Filtration for the Improvement of River Water Quality","year":2005,"lang":"en","type":"article","venue":"Transport in Porous Media","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Floodplain; Environmental science; Hydrology (agriculture); Filtration (mathematics); Water quality; Infiltration (HVAC); Surface water; Organic matter; Hydrogeology; Environmental engineering; Soil science; Geology; Geography; Geotechnical engineering; Mathematics; Chemistry; Ecology","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":[0.0004838617,0.0006273483,0.0009871677,0.0005397651,0.0007473963,0.001136009,0.0008501104,0.002014966,0.0009611634],"category_scores_gemma":[0.001467551,0.0004965658,0.0008806591,0.0006208967,0.0006705048,0.00103318,0.0006205119,0.0006667849,0.00007247993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001905552,"about_ca_system_score_gemma":0.001968126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1021342,"about_ca_topic_score_gemma":0.04797256,"domain_scores_codex":[0.999868,0.00004375253,0.000006923567,0.00003371346,0.00001690963,0.00003061254],"domain_scores_gemma":[0.999376,0.000417089,0.00005885289,0.00002302197,0.00007812275,0.00004694399],"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":[0.00001502428,0.00001869779,0.0007478485,0.000008068369,0.00001018671,0.00001114173,0.00001236122,0.9967037,0.0005765768,0.0006484464,0.00006304706,0.001184876],"study_design_scores_gemma":[0.000003429553,0.000003444811,0.00008428407,5.777135e-7,0.000002715549,0.000001148532,0.00000311614,0.9995515,0.0001639068,0.0001496202,0.00003423692,0.000002102522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6893651,0.0006444987,0.3036802,0.0006901283,0.0001087573,0.00006416586,0.0006062569,0.0008423395,0.003998688],"genre_scores_gemma":[0.9841224,0.0002038559,0.01373016,0.00003176917,0.00001907655,0.0000401711,0.0001022599,0.00004126477,0.001708985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1021342,"threshold_uncertainty_score":0.2030795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01744014315494094,"score_gpt":0.2357366936767252,"score_spread":0.2182965505217843,"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."}}