{"id":"W2040141482","doi":"10.1016/j.ecoleng.2005.10.006","title":"Performance evaluation and effects of hydraulic retention time and mass loading rate on treatment of woodwaste leachate in surface-flow constructed wetlands","year":2006,"lang":"en","type":"article","venue":"Ecological Engineering","topic":"Constructed Wetlands for Wastewater Treatment","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydraulic retention time; Leachate; Chemical oxygen demand; Environmental science; Mesocosm; Biomass (ecology); Environmental engineering; Wetland; Water retention; Environmental chemistry; Subsurface flow; Chemistry; Pulp and paper industry; Nutrient; Wastewater; Soil science; Ecology; Soil water; Groundwater; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001566336,0.0001460822,0.000235183,0.00005519862,0.00002336958,0.00000701541,0.0000314824,0.00006587533,0.00008039614],"category_scores_gemma":[0.00001915731,0.0001120452,0.00002487816,0.0001239733,0.00006686072,0.00007457207,0.00002686322,0.0000425715,0.000005033899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001900918,"about_ca_system_score_gemma":0.000003539039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000405395,"about_ca_topic_score_gemma":0.00001289572,"domain_scores_codex":[0.9992244,0.0000453445,0.0002227995,0.0002132282,0.0001261993,0.0001680412],"domain_scores_gemma":[0.9996862,0.0001161622,0.00007133911,0.00008423736,0.000005695703,0.00003639559],"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":[0.00005006321,0.0001036525,0.3213712,0.00004263511,0.00002047594,0.000008981861,0.0000766181,0.3237077,0.3442184,0.000006639599,0.000001077821,0.01039258],"study_design_scores_gemma":[0.001240326,0.0004823713,0.5196944,0.00004237196,0.00002279677,0.000004688788,0.000003054751,0.4278103,0.05060425,0.00001941368,0.000003381359,0.00007260811],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992173,0.0000470808,0.00002051696,0.0000115319,0.00004109235,0.0003803351,0.000003609558,0.00002000866,0.0002584717],"genre_scores_gemma":[0.9987558,0.00004219929,0.001125614,0.000001288607,0.000005622258,0.00001632505,0.0000150993,0.000006813542,0.00003126598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2936141,"threshold_uncertainty_score":0.4569072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005378119555689398,"score_gpt":0.181293082626085,"score_spread":0.1759149630703956,"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."}}