Passive biological treatment of acid mine drainage: challenges of the 21st century
Bibliographic record
Abstract
Acid mine drainage (AMD), characterized by a low pH and high concentrations of sulphates and heavy metals, is a disquieting problem for the Canadian mineral industry and other industries elsewhere in the world. Traditional active systems, including lime neutralization, become costly in time or inapplicable in remote regions. Research has recently focussed on passive biological systems that have certain advantages such as low installation, operation and maintenance costs. The three groups of promising passive biotechnologies are wetlands, bioreactors, and permeable reactive walls. Their efficiency is sometimes limited as it depends on the activity of the sulphatereducing bacteria (SRB), which is in turn mainly controlled by the composition of the reactive mixture. The essential component of the reactive mixture is organic matter, which must be inexpensive, relatively biodegradable and available in the long term. The components of the reactive mixture must also allow for adequate flow within the system. Performance of the passive biological reactors is also related to the initial AMD load and the toxicity of the metals present. Several reactive mixtures were tested to find sources of organic matter that are both reactive and available in the long term. However, speciation of metals in effluents and in the reactive mixture and the toxicity of the treated effluents still need to be studied. Many challenges thus remain for a better prediction of the passive biological system efficiency.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".