Passive biological treatment of acid mine drainage: challenges of the 21st century
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
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.
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 | 0.000 |
| 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 it