Biogeochemical Behavior of Metals Along Two Permeable Reactive Barriers in a Mining‐Affected Wetland
Bibliographic record
Abstract
Abstract The biogeochemistry of two alkaline permeable reactive barriers (PRBs) installed for remediation in a mining‐affected wetland was investigated in order to assess the importance of colloidal particles on metal removal processes in such systems. At the time of investigation, both PRBs were effective in removing U, Cu, and Zn (>95%) from groundwater but were slightly less efficient for Ni and Co (<90%). Previously installed groundwater wells allowed an in‐depth analysis of groundwater passing through the first PRB. Here, in an alkaline environment (pH 6.0–9.7), 11–14% of Ni, 36–37% of Co, 77–81% of Cu, 14–17% of U, and 10–19% of Fe were associated with organic matter and inorganic colloids, while upgradient in the more acidic environments (pH <6.0), ionic species and complexes (e.g., Co2+, Ni2+, Cu2+, and UO2H3SiO4+) dominated. Copper and U preferentially bound to larger colloidal fractions (>1 kDa), which might have promoted their sequestration. Uranium removal was likely further enhanced by U (VI) reduction in the alkaline and oxygen‐depleted conditions of the PRBs. The less efficient removal of Ni and Co, being target metals for remediation, was explained by a combination of their high solubility, unfavorable redox and pH conditions created by the alkaline PRBs, and their limited association with colloidal particles. These considerations are critical in the design of future PRBs for the remediation of similar systems.
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.
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) | 0.000 | 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) | 0.000 | 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 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".