{"id":"W2083161036","doi":"10.1016/j.desal.2014.04.014","title":"Hardness, COD and turbidity removals from produced water by electrocoagulation pretreatment prior to Reverse Osmosis membranes","year":2014,"lang":"en","type":"article","venue":"Desalination","topic":"Advanced oxidation water treatment","field":"Environmental Science","cited_by":206,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; Petroleum Technology Research Centre; University of Regina","keywords":"Turbidity; Chemical oxygen demand; Reverse osmosis; Electrocoagulation; Chemistry; Response surface methodology; Fouling; Pulp and paper industry; Environmental engineering; Effluent; Pollutant; Coagulation; Water treatment; Electrolysis; Membrane; Chromatography; Environmental science; Wastewater; Electrode","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":[0.00009319897,0.0002363246,0.0002445674,0.0001493019,0.0001998892,0.0003279368,0.0001656093,0.000293532,0.001257043],"category_scores_gemma":[0.0002940903,0.0001460553,0.0002392964,0.00017347,0.0001903765,0.0002726803,0.0001425325,0.0004382354,0.0001918413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002532332,"about_ca_system_score_gemma":0.0002525532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002544312,"about_ca_topic_score_gemma":0.003905187,"domain_scores_codex":[0.9998651,0.000009277912,0.00001143461,0.00002492785,0.00005090016,0.0000383444],"domain_scores_gemma":[0.9998981,0.00002261014,0.00002495298,0.00001024399,0.0000310829,0.00001306861],"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":[0.0002660124,0.00002031679,0.00044156,0.00002288319,0.000004955354,0.00004208871,0.00002522871,0.0001285987,0.9975523,0.00001767062,0.00002100977,0.00145727],"study_design_scores_gemma":[0.000005999597,0.0001533364,0.005280403,0.000001503508,0.000008689461,0.00002341067,0.00002945488,0.0004666546,0.9937282,0.00001138615,0.0002861556,0.000004901839],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990226,0.00007777887,0.0004324785,0.00001521275,0.000008312981,0.00000444773,0.00003742792,0.000009991058,0.0003916894],"genre_scores_gemma":[0.9981512,0.00006724559,0.0004055132,0.00001040007,0.000003047504,0.000004143704,0.00007368858,0.000007317746,0.001277398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002544312,"threshold_uncertainty_score":0.005058944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005478862434577346,"score_gpt":0.2128765954260246,"score_spread":0.2073977329914472,"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."}}