{"id":"W2413606743","doi":"10.7603/s40872-015-0005-4","title":"Treatment of High Strength Vegetable Processing Wastewater with a Sequencing Batch Reactor","year":2016,"lang":"en","type":"article","venue":"GSTF Journal on Agricultural Engineering","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Toronto","funders":"Ontario Ministry of Food and Agriculture; Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Kjeldahl method; Wastewater; Chemical oxygen demand; Sequencing batch reactor; Pulp and paper industry; Hydraulic retention time; Chemistry; Phosphorus; Nitrogen; Environmental science; Environmental engineering; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000456072,0.0005308813,0.0006212457,0.0002503624,0.0002929318,0.0005132928,0.0004092576,0.0005348399,0.000533819],"category_scores_gemma":[0.0003049832,0.0002125446,0.0004866059,0.0002699729,0.0001940251,0.0002470342,0.0002064557,0.0003521211,0.0002768644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003959468,"about_ca_system_score_gemma":0.0005309451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003463608,"about_ca_topic_score_gemma":0.003613863,"domain_scores_codex":[0.9996588,0.00008421759,0.00004100431,0.0000631545,0.0001195593,0.00003324417],"domain_scores_gemma":[0.9998165,0.00005139207,0.00003440446,0.00001502452,0.00004565834,0.00003707433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008234317,0.00004226534,0.0001548586,0.00004355927,0.000006251665,0.00002398127,0.00001007206,0.0004311856,0.9981816,0.00001132413,0.000008353741,0.001004254],"study_design_scores_gemma":[0.00002525756,0.0008128504,0.002050094,0.000006554646,0.00003604529,0.0001016284,0.00003343575,0.003852372,0.992404,0.00003049154,0.0006332686,0.00001392053],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902653,0.0002624711,0.00852186,0.00004918421,0.00002837011,0.00007153006,0.0001410929,0.00007871557,0.0005815744],"genre_scores_gemma":[0.9824316,0.0003874224,0.01539465,0.00003278672,0.00001292584,0.0000544762,0.0002330002,0.00002168842,0.001431551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003463608,"threshold_uncertainty_score":0.006886899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007410006290422154,"score_gpt":0.172719374876881,"score_spread":0.1653093685864588,"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."}}