Drought and liming impacts of mine-impacted wetland sediments
Bibliographic record
Abstract
Acid mine drainage (AMD) is a serious environmental problem at legacy and active mine sites around the world. Climate associated drought and rewetting events can increase the severity of AMD impacts through oxidation and release of stored metal(loid)s and acidity from contaminated sediments. The area surrounding Sudbury, Ontario, with its massive mining and smelting complexes, appears especially vulnerable to drought-driven effects. The impacts of drought-rewetting cycles in the heavily mine-impacted Frood subwatershed of Junction Creek, flowing through the centre of Sudbury and containing a large mine site and waste rock storage area was of particular interest, as drought and flood management strategies are being proposed. Laboratory drying and re-wetting experiments with sediment-cores collected from the Nickledale wetland in the Frood branch surprisingly showed no post-drought re-acidification (pH<6), with average pH among all drought treatments being 7.25 ± 0.20. However, highest concentrations of many metal(loid)s were observed in the longest (60-day) drought treatment, suggesting a drought effect may still occur. These included [Ni] (approx. 100x control), [Cu] (approx. 10x control), [Cd] (approx. 65x control) and [Co] (approx. 200x control), all depicting post-drought release from sediment. Pre-drought lime treatments showed reduced concentrations of many metal(loid)s in-solution following re-wetting. This included average [Cd], [Co], [Cu] and [Ni] observed to be significantly less in the lime-plus-30-day-drough treatment (L30d) compared to 30 day drought only treatment (30d) (RM ANOVA, p ≤ 0.007 [Cd]; p ≤ 0.004 [Co]; p = 0.022 [Cu]; p ≤ 0.009 [Ni]) (Figure 2a; Table A1 (supplementary)). This study suggests that the remedial work at the mine impacted site has reduced the vulnerability of this subwatershed to climate-driven impacts.
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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 | 0.000 | 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) | 0.001 | 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".