{"id":"W2558470050","doi":"10.2166/wst.2016.556","title":"Membrane technology applied to acid mine drainage from copper mining","year":2016,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Mine drainage and remediation techniques","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias; Comisión Nacional de Investigación Científica y Tecnológica","keywords":"Acid mine drainage; Nanofiltration; Membrane; Reverse osmosis; Copper; Chemistry; Drainage; Sulfate; Membrane technology; Permeation; Manganese; Environmental engineering; Environmental science; Environmental chemistry; Biochemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003029327,0.0003578912,0.0004257032,0.0002597056,0.000375616,0.0004383664,0.000233735,0.0005910959,0.0004043492],"category_scores_gemma":[0.0003881469,0.0001297552,0.0004397052,0.0002845627,0.0002116885,0.0005273899,0.0003094734,0.0003113876,0.0001945047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005125505,"about_ca_system_score_gemma":0.0004112908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001650494,"about_ca_topic_score_gemma":0.001450236,"domain_scores_codex":[0.999769,0.00005298917,0.00001868941,0.00004036909,0.00008464851,0.0000342443],"domain_scores_gemma":[0.9999144,0.00001980465,0.0000185345,0.000007017019,0.00003226228,0.000007958713],"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":[0.00004286849,0.00001966359,0.0004992635,0.0001942638,0.000009286783,0.0001300192,0.00003115494,0.0002730057,0.9906669,0.00006191806,0.00002780841,0.00804374],"study_design_scores_gemma":[0.00001149199,0.0004381964,0.002868451,0.00002266309,0.00003416001,0.0007305227,0.0000863136,0.001101468,0.9896811,0.0001040583,0.004911664,0.00001003121],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802491,0.004911328,0.0125011,0.000185999,0.00006837982,0.00008265622,0.00007007755,0.00006454553,0.001866917],"genre_scores_gemma":[0.9828173,0.004687922,0.01130681,0.00004671015,0.00001742626,0.00004037714,0.00009920191,0.00001044729,0.0009737873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001650494,"threshold_uncertainty_score":0.003718853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006005679450369131,"score_gpt":0.2145238541707233,"score_spread":0.2085181747203542,"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."}}