The impacts of whole-lake acidification and eutrophication on the accumulation of lead in sediments from manipulated lakes in the Experimental Lakes Area (IISD-ELA)
Bibliographic record
Abstract
Acidification and eutrophication are common limnological stressors impacting many water bodies across the globe. While the negative impacts of these stressors on limnetic communities are generally known, their influence on the accumulation of specific sediment constituents, such as metals, remains unclear. Benefitting from past research and long-term monitoring, lakes at the International Institute for Sustainable Development - Experimental Lakes Area (IISD-ELA) in northwestern Ontario, Canada are invaluable to understand the extent to which these two common lake stressors can influence the accumulation of metals in lacustrine sediment. To address these issues, sediment cores were retrieved from six lakes: four were subjected to past experimental acidification or eutrophication and two were reference lakes. Focusing on elemental lead (Pb), a metal known to have accumulated in lake sediments worldwide and generally exhibiting a relatively small fraction of terrigenous input, we assessed the hypothesis that greater accumulation of Pb would be observed in lakes subjected to eutrophication, while the reverse was expected for lakes subjected to acidification experiments. Our analyses support this hypothesis, whereby relatively low enrichment was recorded in sediments deposited in the acidified lake during the manipulation era. On the other hand, eutrophied lakes demonstrated a strong enrichment in Pb during experimental manipulation. When investigating the mechanisms behind these divergent responses, we found epilimnetic dissolved organic carbon (DOC) and conductivity were associated with a relative increase in Pb accumulation in sediments. Acidic pH is also expected to mediate these responses by decreasing epilimnetic DOC concentrations leading to reduced Pb accumulation in the sediment.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".