{"id":"W2886725290","doi":"10.1016/j.mineng.2018.07.014","title":"Influence of ferric iron source on ferrate’s performance and residual contamination during the treatment of gold mine effluents","year":2018,"lang":"en","type":"article","venue":"Minerals Engineering","topic":"Water Treatment and Disinfection","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Technologique des Résidus Industriels; Cégep de l'Abitibi Témiscamingue; Dawson College; Université du Québec en Abitibi-Témiscamingue","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Contamination; Effluent; Ferric; Residual; FERRIC IRON; Waste management; Environmental science; Iron ore; Metallurgy; Environmental chemistry; Chemistry; Environmental engineering; Materials science; Engineering; Ferrous; Computer science; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004701379,0.0001126015,0.000106488,0.00004173554,0.00004248219,0.000007446085,0.00004886325,0.00002834923,0.00001412469],"category_scores_gemma":[0.000008528212,0.00007607348,0.00001699602,0.0001217953,0.00006307527,0.0001163557,0.00002083195,0.00002135381,0.00001319381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008208951,"about_ca_system_score_gemma":0.000001365103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002561005,"about_ca_topic_score_gemma":0.00003155229,"domain_scores_codex":[0.999469,0.00001025671,0.0001426279,0.0001311564,0.0001232176,0.0001236777],"domain_scores_gemma":[0.9997646,0.00002116768,0.00005321476,0.0001252069,0.000009529338,0.00002631255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004896933,0.0001177657,0.2666351,0.00003640554,0.00001470759,7.099021e-7,0.0009823581,0.185106,0.5460833,0.00000551624,0.00002487896,0.0009443362],"study_design_scores_gemma":[0.0004622112,0.0005731241,0.7919205,0.00003096845,0.00001494817,0.000002913037,0.00000828374,0.003962588,0.202718,5.886739e-7,0.0002397192,0.00006603138],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999468,0.0000251646,0.000009825144,0.00001753519,0.00002987522,0.0001425248,0.000001730444,0.00001745122,0.0002878552],"genre_scores_gemma":[0.9982365,0.00003922726,0.00004512252,0.000004070942,0.00003466664,0.00001359261,0.000003447337,0.00000854394,0.001614783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5252855,"threshold_uncertainty_score":0.3102187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005391104332984896,"score_gpt":0.1891208282418095,"score_spread":0.1837297239088246,"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."}}