{"id":"W2991902799","doi":"","title":"Removal of Chromium, Nickel, Zinc and Turbidity from Industrial Wastewater by Electrocoagulation Technology (Case Study: Electroplating and Galvanized Wastewater of Industrial Zone in Boomhen)","year":2015,"lang":"en","type":"article","venue":"Majallah-i dānishgāh-i ̒ulūm-i pizishkī-i Māzandarān/Journal of Mazandaran University of Medical Sciences","topic":"Advanced oxidation water treatment","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wastewater; Electrocoagulation; Chromium; Galvanization; Turbidity; Zinc; Plating (geology); Metallurgy; Industrial wastewater treatment; Nickel; Pulp and paper industry; Environmental science; Environmental engineering; Materials science; Geology; Composite material","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003946661,0.0004781546,0.0006341258,0.0005862204,0.0007347045,0.0008320402,0.0005990572,0.00109507,0.0005051799],"category_scores_gemma":[0.0002710192,0.0002186153,0.0007062511,0.0006027843,0.0003997369,0.0003730692,0.0004575558,0.0003018277,0.0002320003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005718191,"about_ca_system_score_gemma":0.0003668183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003999235,"about_ca_topic_score_gemma":0.006136303,"domain_scores_codex":[0.9995342,0.0000771554,0.00004101063,0.0001101738,0.0001695566,0.00006791308],"domain_scores_gemma":[0.999864,0.00003205163,0.00003557476,0.00001214768,0.00004314212,0.00001307679],"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":[0.0004013091,0.0004085329,0.01265224,0.0006014692,0.000055647,0.002803068,0.0005692267,0.002612516,0.9548164,0.00008263821,0.0001282134,0.02486865],"study_design_scores_gemma":[0.00006377995,0.003536859,0.05011136,0.00004261013,0.0001467588,0.003196483,0.001004368,0.006262157,0.933021,0.0001171197,0.002450704,0.00004696214],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983753,0.000243055,0.0009438644,0.00003134404,0.000003803675,0.00002729111,0.0000211449,0.00001335209,0.0003409014],"genre_scores_gemma":[0.9949922,0.0004413066,0.003315286,0.00001922804,0.000004716203,0.0000226471,0.00005858965,0.00000677925,0.001139164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003999235,"threshold_uncertainty_score":0.007951915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03590362578723456,"score_gpt":0.2531285754259111,"score_spread":0.2172249496386766,"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."}}