Effects of drought‐induced acidification on diatom communities in acid‐sensitive Ontario lakes
Bibliographic record
Abstract
Chemical recovery of many acidified lakes in North America has been delayed or reversed as a result of interactions between climatic variability (alterations between drought and nondrought periods) and previously deposited acids stored in wetlands, but effects of this wetland‐mediated reacidification phenomenon on aquatic biota remain unknown. We compare changes in diatom assemblages in 200‐yr‐long sediment cores from two lakes with similar basin characteristics but different wetland area (4.4% of catchment at Chub Lake, 0% at Blue Chalk Lake) to evaluate the role of wetland‐mediated interactions among acid deposition and climatic variability on algal communities in acid‐sensitive lakes. Diatom assemblages were significantly more variable in Chub Lake than in Blue Chalk Lake. Variance partitioning analysis of approximately annually resolved sedimentary diatom analyses (1977–1997) identified that unique effects of water chemistry, independent of acid deposition and climatic factors, accounted for the greatest amount of variation (25%) in diatom assemblages at Chub Lake. Acid deposition (24%) and climatic factors (22%) accounted for similar, significant amounts of variation in diatom communities. Complex interactions among all three factors, which are attributable to wetland‐mediated drought‐induced reacidification, explained an additional 10% of the variation in diatoms at Chub Lake but only 1% at Blue Chalk Lake. Droughtrelated reacidification effects on water chemistry might thus cause important effects on algal communities in acid sensitive lakes with modest wetland coverage, but not in lakes without wetlands.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".