Treatment of Mine Drainage Using Permeable Reactive Barriers: Column Experiments
Bibliographic record
Abstract
Permeable reactive barriers designed to enhance bacterial sulfate reduction and metal sulfide precipitation have the potential to prevent acid mine drainage and the associated release of dissolved metals. Two column experiments were conducted using simulated mine-drainage water to assess the performance of organic carbon-based reactive mixtures under controlled groundwater flow conditions. The simulated mine drainage is typical of mine-drainage waterthat has undergone acid neutralization within aquifers. This water is near neutral in pH and contains elevated concentrations of Fe(II) and SO4. Minimum rates of SO4 removal averaged between 500 and 800 mmol d(-1) m(-3) over a 14-month period. Iron concentrations decreased from between 300 and 1200 mg/L in the influent to between <0.01 and 220 mg/L in the columns. Concentrations of Zn decreased from 0.6-1.2 mg/L in the input to between 0.01 and 0.15 mg/L in the effluent, and Ni concentrations decreased from between 0.8 and 12.8 mg/L to <0.01 mg/L. The pH increased slightly from typical input values of 5.5-6.0 to effluent values of 6.5-7.0. Alkalinity, generally <50 mg/L (as CaCO3) in the influent, increased to between 300 and 1,300 mg/L (as CaCO3) in the effluent from the columns. As a result of decreased Fe(II) concentrations and increased alkalinity, the acid-generating potential of the simulated mine-drainage water was removed, and a net acid-consuming potential was observed in the effluent water.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".