{"id":"W2342537569","doi":"10.3390/w8050183","title":"Performance of a Constructed Wetland and Pretreatment System Receiving Potato Farm Wash Water","year":2016,"lang":"en","type":"article","venue":"Water","topic":"Constructed Wetlands for Wastewater Treatment","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Wilfrid Laurier University; Agriculture and Agri-Food Canada","funders":"Ministry of Agriculture, Food and Rural Affairs; Agriculture and Agri-Food Canada; Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph","keywords":"Environmental science; Wetland; Aeration; Wastewater; Total suspended solids; Sewage treatment; Constructed wetland; Phosphorus; Sedimentation; Suspended solids; Biochemical oxygen demand; Environmental engineering; Hydrology (agriculture); Animal science; Sediment; Chemistry; Chemical oxygen demand; Ecology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002271973,0.0004103984,0.0004488904,0.0001736855,0.0005688879,0.0004338736,0.0004713743,0.0004199674,0.001059609],"category_scores_gemma":[0.0003117435,0.0001685412,0.0003323954,0.0002249262,0.000300131,0.0002529248,0.0003055216,0.0002680958,0.0002999679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001296476,"about_ca_system_score_gemma":0.001942162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1186556,"about_ca_topic_score_gemma":0.1025022,"domain_scores_codex":[0.9997453,0.000020413,0.00001680137,0.00006966751,0.00007274473,0.00007514414],"domain_scores_gemma":[0.9998393,0.00001937707,0.00002066652,0.00001140648,0.00005772603,0.00005162515],"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":[0.001112107,0.0004350719,0.007148068,0.0001522417,0.00002101224,0.0001383962,0.0001455143,0.002158381,0.9794803,0.00003472199,0.0001801435,0.00899401],"study_design_scores_gemma":[0.0002375034,0.007926381,0.1244459,0.00001760822,0.0001249887,0.0001841134,0.0006118526,0.02222474,0.8409999,0.00004153043,0.003101622,0.00008381269],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987914,0.00002558768,0.0006662397,0.00001178869,0.000005887983,0.00006057466,0.00009649489,0.00004008902,0.0003021063],"genre_scores_gemma":[0.9959335,0.00007754117,0.002509074,0.00001839198,0.00000348247,0.00005796112,0.0002665899,0.000009951144,0.001123585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1186556,"threshold_uncertainty_score":0.2359298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004435515716591325,"score_gpt":0.1716804349483012,"score_spread":0.1672449192317099,"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."}}