{"id":"W37476390","doi":"10.1007/s10230-012-0199-z","title":"Capacity of Wood Ash Filters to Remove Iron from Acid Mine Drainage: Assessment of Retention Mechanism","year":2012,"lang":"en","type":"article","venue":"Mine Water and the Environment","topic":"Mine drainage and remediation techniques","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Université du Québec en Abitibi-Témiscamingue; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Sorption; Acid mine drainage; Wood ash; Hydraulic conductivity; Freundlich equation; Effluent; Chemistry; Environmental chemistry; Waste management; Adsorption; Environmental engineering; Environmental science; Soil water","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":[0.0004508241,0.0002165684,0.0001830619,0.0002417071,0.0002567554,0.0002980425,0.0003516357,0.0004344292,0.0008147283],"category_scores_gemma":[0.000668928,0.0001435335,0.0002160817,0.0001201168,0.0002244176,0.000307989,0.0001353328,0.0001985981,0.000222608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002244626,"about_ca_system_score_gemma":0.0003161221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002831823,"about_ca_topic_score_gemma":0.002592583,"domain_scores_codex":[0.9998574,0.00002163279,0.000007605878,0.00002172755,0.00004856639,0.00004299873],"domain_scores_gemma":[0.9995431,0.0002367126,0.00004595755,0.00003330038,0.0001024949,0.00003850558],"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":[0.0005207769,0.00008509285,0.003048988,0.00005915422,0.00002115248,0.00003076943,0.0001007193,0.0005929684,0.9866202,0.0001005492,0.00006802502,0.008751646],"study_design_scores_gemma":[0.00001281204,0.0005436817,0.009756069,0.000007175348,0.00002298931,0.00007638567,0.00007690354,0.002289771,0.9865199,0.00004609319,0.000640488,0.000007783762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982886,0.0002214781,0.0009924955,0.00001353944,0.00000458505,0.000006830278,0.00004889484,0.00001142849,0.0004121334],"genre_scores_gemma":[0.9978424,0.000174986,0.00059927,0.00001216717,0.000003379867,0.000007105795,0.0001162038,0.000006007397,0.001238445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002831823,"threshold_uncertainty_score":0.005630672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009901586174997928,"score_gpt":0.2095712267955992,"score_spread":0.1996696406206013,"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."}}