Bench-scale comparison of conventional and high rate clarification treatment processes for acid mine drainage
Bibliographic record
Abstract
Acid mine drainage (AMD) is characterized as having low pH and high concentrations of sulfate and dissolved metals. This study compared treated water quality and sludge properties of three process technologies for AMD: conventional sedimentation, high density sludge (HDS), and ballasted flocculation. All three processes were found to be capable of removing regulated metals to concentrations below current Canadian discharge guidelines. However, ballasted flocculation was the only technology found to be able to meet the more stringent federal guidelines proposed for future implementation under the Fisheries' Act's Metal Mining Effluent Regulations. Specifically, arsenic and zinc concentrations in AMD treated by the conventional and HDS processes were above proposed future guidelines of 0.10 and 0.25 mg/L, respectively, while lead, copper, and nickel all met respective guidelines. Concentrations of all regulated contaminants were below proposed guidelines when treated by ballasted flocculation. The HDS process was found to produce a significantly more concentrated sludge than conventional sedimentation (i.e., higher solids content (19 ± 1% versus 7 ± 4% wet solids) and lower sludge volume index (SVI; 8.4 ± 0.8 versus 230 ± 20 mL/g)).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".