{"id":"W4205836167","doi":"10.3390/w14010039","title":"Treatment of Actual Winery Wastewater by Fenton-like Process: Optimization to Improve Organic Removal, Reduce Inorganic Sludge Production and Enhance Co-Treatment at Municipal Wastewater Treatment Facilities","year":2021,"lang":"en","type":"article","venue":"Water","topic":"Advanced oxidation water treatment","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Ryerson University","keywords":"Sewage treatment; Wastewater; Environmental science; Winery; Activated sludge; Chemical oxygen demand; Suspended solids; Effluent; Waste management; Sewage; Settling; Sequencing batch reactor; Total suspended solids; Pulp and paper industry; Environmental engineering; Chemistry; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006045194,0.0006242911,0.000533519,0.0000771725,0.0002665064,0.00006777333,0.0001297767,0.0001305811,0.002629113],"category_scores_gemma":[0.00001339545,0.0003987789,0.0001008357,0.0001802424,0.0001899033,0.0005285222,0.0001681905,0.00004425913,0.0004493925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002686972,"about_ca_system_score_gemma":0.00003560209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003148805,"about_ca_topic_score_gemma":0.0002956479,"domain_scores_codex":[0.9970207,0.0001215566,0.0005768199,0.001220767,0.0003951227,0.0006649987],"domain_scores_gemma":[0.998767,0.00002188183,0.0001343383,0.000781806,0.00005120988,0.0002437813],"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":[0.0002890417,0.001119121,0.001781923,0.00003262178,0.0001798754,0.00003870552,0.02274465,0.01006618,0.9615002,2.175833e-7,0.0001941539,0.002053315],"study_design_scores_gemma":[0.001402483,0.001918403,0.0001078898,0.0000201176,0.0001308832,0.0001177876,0.001214349,0.0001895002,0.9806943,0.00001033735,0.01377581,0.0004181374],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968753,0.0001818421,0.00007634658,0.0009598151,0.0002647695,0.001253171,0.0001514743,0.00008620516,0.000151092],"genre_scores_gemma":[0.9209555,0.000493199,0.002416422,0.00004152368,0.00007432365,0.0004052105,0.0008109402,0.00007406595,0.0747288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07591977,"threshold_uncertainty_score":0.9998464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0114453413434052,"score_gpt":0.2465795727461912,"score_spread":0.235134231402786,"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."}}