{"id":"W3016773576","doi":"10.1021/acs.est.0c00012","title":"Rapid and Efficient Arsenic Removal by Iron Electrocoagulation Enabled with in Situ Generation of Hydrogen Peroxide","year":2020,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Basic Energy Sciences; Philippine-California Advanced Research Institutes; Office of Science; Tobacco-Related Disease Research Program; Office of the President, University of California; Canada Excellence Research Chairs, Government of Canada; U.S. Department of Energy; SLAC National Accelerator Laboratory; Commission on Higher Education; National Science Foundation","keywords":"Arsenic; Electrocoagulation; Electrolysis; Chemistry; Hydrogen peroxide; Contaminated groundwater; Environmental chemistry; Groundwater; Water treatment; Inorganic chemistry; Nuclear chemistry; Environmental engineering; Environmental science; Contamination; Electrode; Environmental remediation; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001473592,0.0004196847,0.0003013866,0.0002103804,0.0001648976,0.0003486852,0.0003141746,0.0003750682,0.0007970527],"category_scores_gemma":[0.0001552899,0.0002194318,0.0002412027,0.0002082845,0.0001676219,0.0002915207,0.0002665404,0.0004270752,0.0002579742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000194429,"about_ca_system_score_gemma":0.0001746282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0020467,"about_ca_topic_score_gemma":0.003416844,"domain_scores_codex":[0.9998201,0.00001222652,0.00001064887,0.0000494317,0.00006825192,0.00003945811],"domain_scores_gemma":[0.9999439,0.000009088034,0.00001328625,0.00000769217,0.00001897036,0.000007145685],"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":[0.00003205367,0.00001822276,0.0001473429,0.00005083814,0.000004231309,0.00004193893,0.00002188294,0.0001188409,0.9958402,0.00003722953,0.0001084421,0.003578747],"study_design_scores_gemma":[0.000004883993,0.00009197561,0.0007201445,0.000001758611,0.000005253219,0.00004703975,0.00001456176,0.001116458,0.9967211,0.000011553,0.001260902,0.000004473925],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824163,0.00128257,0.01254628,0.0001302548,0.00005407266,0.00004966729,0.0001833661,0.0004258769,0.002911679],"genre_scores_gemma":[0.9894299,0.0006435859,0.007338992,0.00004892673,0.000008039278,0.00001650451,0.0001647868,0.00003354296,0.002315663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0020467,"threshold_uncertainty_score":0.004069567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005862237223925031,"score_gpt":0.1842949839947709,"score_spread":0.1784327467708459,"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."}}