Passive and semi-passive treatment alternatives for the bioremediation of selenium from mine waters
Bibliographic record
Abstract
The bioremediation of selenium (Se) from mine waters using passive systems (those requiring negligible management) and semi-passive systems (those requiring active management to sustain desired conditions and processes) is reviewed. Examples of passive systems include natural wetlands, constructed surface-flow wetlands, constructed subsurface-flow wetlands and permeable reactive barriers (PRB). Examples of semi-passive systems, such as in situ pit lake treatment, require active management that may involve periodic amendments (e.g., organic carbon, nutrients) to stimulate desired microbial mechanisms. In all cases, Se bioremediation relies primarily upon microbial and/or biological processes to remove Se from solution, including plant uptake, precipitation (e.g., in situ formation of elemental Se), adsorption, microbial/algal assimilation and biological volatilization (e.g., release of dimethyl selenide to atmosphere). Case studies that describe field-scale examples of passive and semi-passive bioremediation for Se are presented. Considerations for Se bioremediation in interior temperate climates (e.g. Elk River Valley region) as they relate to constructed/natural wetlands, pond environments (e.g. sedimentation ponds), pit lakes and PRBs are discussed. Key words: selenium, bioremediation, passive, semi-passive, mining
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".