Iron removal in highly contaminated acid mine drainage using passive biochemical reactors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Passive biochemical reactors (PBRs) are a viable alternative to neutralization plants for the treatment of acid mine drainage (AMD) because they require lower investment costs and use residual materials. However, high iron (Fe) concentrations (≥0.5 g/L) in AMD are challenging for their long-term efficiency. Sorption and precipitation are the main Fe removal mechanisms, but the relative importance of each is mostly unknown. In this study, locally available natural materials (organic and inorganic) were characterized and tested for their performance in Fe removal from highly contaminated AMD (pH 3.5, 4 g/L of Fe, and 9 g/L of sulfate). Iron retention capacity of the materials was then evaluated and the efficiency of eight mixtures of materials was compared through 40-day laboratory batch tests. All batch-type PBRs increased the pH up to 6.5 and decreased dissolved metals concentrations, including Fe, up to 99%. Results showed that organic residual materials (manures, municipal wastewater sludge, and compost) were the best substrates for Fe removal.These findings allowed for the selection of three reactive mixtures with distinct characteristics (mixture #1 - 30% organic wastes; mixture #4 - 50% calcite; and mixture #7 - 50% sand) to be further evaluated in column type PBRs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it