{"id":"W2996508073","doi":"10.1016/j.watres.2019.115404","title":"In-situ determination of current density distribution and fluid modeling of an electrocoagulation process and its effects on natural organic matter removal for drinking water treatment","year":2019,"lang":"en","type":"article","venue":"Water Research","topic":"Advanced oxidation water treatment","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Royal Economic Society; CMC Microsystems","keywords":"Electrocoagulation; Current density; Current (fluid); Electrode; Mechanics; Flow (mathematics); Organic matter; Volumetric flow rate; Flow velocity; Dissolution; Computational fluid dynamics; Chemistry; Materials science; Analytical Chemistry (journal); Soil science; Environmental science; Environmental engineering; Thermodynamics; Environmental chemistry; Physics","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":[0.0001516911,0.0002071407,0.0001989428,0.0001692956,0.0002037478,0.0002375884,0.0002537501,0.0003033159,0.0006464469],"category_scores_gemma":[0.0002647773,0.0001173951,0.0002083305,0.000155793,0.00018853,0.0003694167,0.0001214852,0.0002805935,0.0000864942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002972675,"about_ca_system_score_gemma":0.0001375414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00125702,"about_ca_topic_score_gemma":0.001636823,"domain_scores_codex":[0.9999075,0.0000117652,0.000005129604,0.00002394417,0.00004041937,0.00001121044],"domain_scores_gemma":[0.999891,0.0000442793,0.00001648405,0.000008817121,0.00003363876,0.00000572935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007908513,0.00003792675,0.000640984,0.00002591093,0.000003721864,0.00002293754,0.00002652481,0.0007964663,0.9961201,0.00007576649,0.00003770697,0.002132945],"study_design_scores_gemma":[0.000004728285,0.00007216608,0.001965053,0.000001264387,0.000005747847,0.00002057373,0.00003199563,0.01926166,0.9782704,0.00003702578,0.0003253101,0.000004102364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896076,0.0001596246,0.009450387,0.0000625569,0.00001637605,0.000008584794,0.0001154071,0.00005812184,0.0005212535],"genre_scores_gemma":[0.997525,0.00009023065,0.001774441,0.000006541313,0.000003471392,0.000006671327,0.00004645836,0.000005733224,0.0005414154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00125702,"threshold_uncertainty_score":0.002499342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02328538487406395,"score_gpt":0.3308401024510223,"score_spread":0.3075547175769583,"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."}}