{"id":"W2924425626","doi":"10.1149/ma2018-02/27/900","title":"Electrocoagulation (EC) for the Removal of Silica from Mining ARD Water Treatment","year":2018,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Metal Extraction and Bioleaching","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Electrocoagulation; Ferrous; Nanofiltration; Dissolved silica; Fouling; Reverse osmosis; Chemistry; Acid mine drainage; Water treatment; Metal ions in aqueous solution; Ferric; Aqueous solution; Filtration (mathematics); Colloid; Pulp and paper industry; Metal; Chemical engineering; Environmental chemistry; Environmental engineering; Membrane; Environmental science; Inorganic chemistry; Dissolution","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":[0.0001025047,0.0002580428,0.000221379,0.0001784038,0.0001396128,0.0001720872,0.0001631058,0.0003303054,0.0005713053],"category_scores_gemma":[0.0001074619,0.0001208644,0.0002529431,0.0001415793,0.0001587532,0.0002177544,0.000233956,0.0002862143,0.0001701598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001659733,"about_ca_system_score_gemma":0.0001011726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005809612,"about_ca_topic_score_gemma":0.001200639,"domain_scores_codex":[0.9998884,0.0000111499,0.00001021518,0.00002781341,0.00004746021,0.00001495194],"domain_scores_gemma":[0.9999534,0.000008893254,0.00001606593,0.00000402557,0.00001237613,0.000005179282],"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":[0.00001378682,0.000004850655,0.00007225027,0.00004864843,0.000004114657,0.00004427742,0.000009387158,0.00004679612,0.9974381,0.0000233854,0.0000270886,0.002267289],"study_design_scores_gemma":[0.000002472553,0.0000907373,0.001056743,0.000003910567,0.000007795863,0.0001733734,0.00001235086,0.0004629792,0.9969299,0.00001317956,0.001242805,0.000003646162],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9707639,0.007763417,0.0185644,0.0001531111,0.00007383533,0.00005484412,0.00009536467,0.0002049519,0.002326059],"genre_scores_gemma":[0.9897248,0.001884868,0.006541377,0.00008729892,0.000008848137,0.00001576352,0.00005766557,0.00001645166,0.001662979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005809612,"threshold_uncertainty_score":0.001911223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02335294790528652,"score_gpt":0.2492069108975995,"score_spread":0.2258539629923129,"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."}}