{"id":"W2007607613","doi":"10.1016/j.ecoleng.2006.03.006","title":"Artificial aeration to increase pollutant removal efficiency of constructed wetlands in cold climate","year":2006,"lang":"en","type":"article","venue":"Ecological Engineering","topic":"Constructed Wetlands for Wastewater Treatment","field":"Environmental Science","cited_by":233,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aeration; Environmental science; Mesocosm; Macrophyte; Effluent; Environmental engineering; Wetland; Constructed wetland; Pollutant; Agronomy; Sewage treatment; Nutrient; Ecology; Biology","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":[0.0001596171,0.0001392453,0.0002011653,0.00008883538,0.00003830846,0.00001531098,0.0001054242,0.00007605751,0.0004864255],"category_scores_gemma":[0.00005796719,0.0001191471,0.00003981248,0.0003993208,0.00005053062,0.00005833847,0.0001162153,0.00008437115,0.0000524343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002126553,"about_ca_system_score_gemma":0.000007709325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002973274,"about_ca_topic_score_gemma":0.0007678844,"domain_scores_codex":[0.9988471,0.00001905264,0.0003768791,0.000247854,0.0001708543,0.0003382794],"domain_scores_gemma":[0.9996835,0.00005721623,0.00004744497,0.0001261251,0.000005033546,0.00008067433],"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":[0.00006336801,0.0003085666,0.1654182,0.00001145689,0.000006038193,0.0001856927,0.00005734789,0.170898,0.6596013,0.001658332,0.00002480471,0.00176687],"study_design_scores_gemma":[0.0009502506,0.0004100206,0.8171469,0.00003855426,0.00002052308,0.00007368255,0.0000470032,0.05054932,0.1296825,0.0001992974,0.0004335498,0.0004483845],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977472,0.000003623994,0.0004303974,0.00005537638,0.00009444355,0.0002627686,0.00002099141,0.00006231719,0.001322827],"genre_scores_gemma":[0.9949978,9.631694e-7,0.004910376,0.00001414517,0.00002412374,0.00001963241,0.00001019358,0.000008037886,0.00001472566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6517287,"threshold_uncertainty_score":0.5326018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00428323630026556,"score_gpt":0.1826581026602095,"score_spread":0.178374866359944,"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."}}