{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002742947,0.0001108603,0.0001255341,0.00003095207,0.0001172172,0.00002632437,0.00006912235,0.00006023593,0.00003143873],"category_scores_gemma":[0.0000707763,0.00006640362,0.00007087878,0.00003426048,0.00002119364,0.00007194354,0.000006932063,0.00006217002,0.00002677472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003857121,"about_ca_system_score_gemma":0.000006911775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001216615,"about_ca_topic_score_gemma":0.00004011556,"domain_scores_codex":[0.9993069,0.00001546041,0.0002626804,0.000122735,0.00009782107,0.000194428],"domain_scores_gemma":[0.9994711,0.0002572559,0.00005863071,0.0001431868,0.00003648491,0.00003330085],"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":[0.00003008446,0.00001607994,0.00003424602,0.000009777745,0.00006278074,0.000001184758,0.000684828,0.01953332,0.9646366,0.000004560669,0.0001358241,0.01485072],"study_design_scores_gemma":[0.0002294774,0.00007615816,0.00175616,0.00003110121,0.00003871068,0.000006264023,0.0001038542,0.02771598,0.9063398,0.0000557097,0.0635572,0.00008961897],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9738871,0.0001660182,0.0002597168,0.0001149526,0.0004625412,0.0001619448,0.00000537695,0.0001048599,0.0248375],"genre_scores_gemma":[0.9948704,0.00001200265,0.004229272,0.00002217502,0.0005900869,0.00001139301,0.0000207894,0.00002028379,0.0002235708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06342138,"threshold_uncertainty_score":0.2707861,"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."}}