Microbial and Nutrient Investigations into the Use of in Situ Layers for Treatment of Tailings Effluent
Bibliographic record
Abstract
The release of acidic drainage, containing high concentrations of dissolved metals, is associated with mining districts throughout the world. Remediation of acidic drainage at active and abandoned mines remains a significant challenge. A potential alternative technique to prevent the release of acidic drainage is the addition of labile organic carbon to mine wastes during deposition, creating large in situ treatment systems. Organic carbon can enhance bacterially mediated sulfate reduction and subsequent metal sulfide precipitation, treating metal-contaminated water prior to discharge from the impoundment. Two laboratory column experiments were conducted using simulated mine drainage water. The columns contained tailings derived from the Kidd Creek Metallurgical site in Timmins, Ontario, and reactive materials mixed to a 4:1 volumetric ratio. The average sulfate reduction rate observed in the woodchip column was 0.009 mmol L(-1) day(-1) g(-1) organic matter and in the pulp waste column 0.018 mmol L(-1) day(-1) g(-1) organic matter. Residence times were 14 days in the woodchip column, resulting in the average removal of 500 mg L(-1) (5.2 mmol L(-1)) SO4 and 60 mg L(-1) (1.1 mmol L(-1)) Fe, and 13 days in the pulp waste column, resulting in the average removal of 600 mg L(-1) (6.2 mmol L(-1)) SO4 and the complete removal of 100 mg L(-1) (1.8 mmol L(-1)) Fe. In both columns, sulfate reduction was coupled with an increase in alkalinity and pH and the complete removal of 80 mg L(-1) (1.2 mmol L(-1)) Zn and other metals. Populations of sulfate-reducing bacteria within both columns increased by 3-4 orders of magnitude, and bacterial activity was up to 5 times greater than in the unamended tailings. The woodchip material contained lower concentrations of labile C, N, and P than the pulp waste, possibly accounting for the lower sulfate reduction rates and metal removal capacity observed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| 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".