Stability of As-rich residues from pilot biofilters for passive treatment of neutral mine drainage: Laboratory vs field testing
Bibliographic record
Abstract
Passive biofilters efficiently treat As-rich contaminated neutral drainage (As-CND), while the management of contaminated residues remains uncertain. Environmental stability of As-CND post-treatment residues from 3 pilot-scale biofilters lab vs field was assessed. Twenty solid samples were collected from four layers of each biofilter (I-1, A, B and I-2), while As removal mechanisms were assessed via physiochemical and mineralogical characterization. Environmental stability was evaluated using static leaching tests (TCLP, SPLP, and FLT). Results showed highly contaminated residues, with concentrations of As up to 3.6 g/kg, and of Fe up to 86.3 g/kg. Metal oxyhydroxides and silicates were the main mineral phases in the three biofilters. The retention of As was controlled by sorption onto Fe (III)-oxyhydroxides in the laboratory biofilter, whereas in field-pilot biofilters As removal involved sorption in the upper layers but precipitation in the form of FeAsS and co-precipitation with metal (Fe and Al)-oxyhydroxides was found in the deeper layers. Static tests results showed compliance (USEPA) classifying the residues of as non-hazardous and suitable for potential co-disposal with municipal waste. The TCLP results also met Quebec's regulation on mine effluents classifying them as low risk. These findings indicate passive biofilters as promising for the control of As-CND.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".