A Permeable Reactive Barrier for Treatment of Heavy Metals
Bibliographic record
Abstract
Historical storage of ore concentrate containing sulfide minerals at an industrial site in British Columbia, Canada, has resulted in widespread contamination of the underlying soil and ground water. The oxidation of sulfide minerals has released significant quantities of heavy metals, including Cu, Cd, Co, Ni, and Zn, into the ground water. A pilot-scale, compost-based, sulfate-reducing permeable reactive barrier was installed in the path of the dissolved heavy-metal plume. The permeable reactive barrier uses sulfate-reducing bacteria to promote precipitation of heavy metals as insoluble metal sulfides. Monitoring over a 21-month period indicated significant removal of heavy metals within the barrier. Copper concentrations declined from a mean concentration of 3,630 pg/L in the influent to a mean concentration within the barrier of 10.5 microg/L, Cd from 15.3 microg/L to 0.2 microg/L, Co from 5.3 microg/L to 1.1 microg/L, Ni from 131 pg/L to 33.0 microg/L, and Zn from 2,410 microg/L to 136 pg/L. Within the lower half of the barrier where tidal influences were more limited and sulfate-reducing conditions were better maintained, mean treatment levels of 2.9 microg/L (Cu), 0.1 microg/L (Cd), 0.4 microg/L (Co), 2.7 microg/L (Ni), and 6.3 microg/L (Zn) were observed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".