{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002194943,0.0001206924,0.0001290959,0.0001240142,0.0001420178,0.000009564415,0.0001821762,0.00007741118,0.0001910388],"category_scores_gemma":[0.00002355193,0.0001120968,0.00001444409,0.0007184806,0.00106342,0.0002062325,0.0001601322,0.000112605,0.00002592935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003008689,"about_ca_system_score_gemma":0.00001384223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002049911,"about_ca_topic_score_gemma":0.00004820364,"domain_scores_codex":[0.9986716,0.0000264272,0.0002178316,0.0004691248,0.0003567111,0.0002583341],"domain_scores_gemma":[0.9996467,0.00000878787,0.0001158764,0.0001456305,0.000002131663,0.00008093749],"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":[0.00001240906,0.00004869449,0.006239594,0.000001405967,0.000001080008,0.000002828182,0.0004526229,0.0009519451,0.968587,0.00008970666,0.00001364273,0.02359909],"study_design_scores_gemma":[0.0006128602,0.0003260591,0.02289652,0.000004392107,0.000007898068,0.00002971097,0.0005026414,0.02367397,0.9506765,0.0000384484,0.001068582,0.0001624279],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969831,0.0001299859,0.000781203,0.001075109,0.000009834665,0.0003077052,0.000002117735,0.00003392615,0.000677065],"genre_scores_gemma":[0.9988827,0.00004279028,0.000855477,0.0001298551,0.000006192467,0.00001582064,0.00001210622,0.000007684697,0.00004744624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02343666,"threshold_uncertainty_score":0.4571174,"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."}}