Short-term responses to watershed logging on biomass mercury and methylmercury accumulation by periphyton in boreal lakes
Bibliographic record
Abstract
In the boreal forest, watershed logging may increase runoff, as well as chemical loading, including nutrient, dissolved organic carbon, and mercury, to lakes. Because they are exposed directly to nutrients and contaminants exported from the watershed, littoral communities such as periphyton may respond quickly to watershed disturbances. The objectives of this study were to evaluate the response of periphyton to watershed logging using a BACI (before–after control–impact) statistical approach and to develop a predictive tool to facilitate the elaboration of practical logging policies aimed at reducing Hg loading to lakes. In this study, we compare the periphyton biomass in 18 boreal Canadian Shield lakes, as well as their total mercury and methylmercury levels. During the ice-free season from 2000 to 2002, eight of these lakes were monitored before and after logging, with the other 10 lakes serving as controls. The BACI statistical analyses reveal a significant impact of logging on periphyton biomass (decrease; 0.6- to 1.5-fold) and methylmercury accumulation (increase; 2- to 9.6-fold). This study demonstrates that periphyton responds quickly to disturbances of the watershed. Our results suggest that the periphyton and watershed characteristics could serve as good management tools and that logging should be limited in watersheds with a mean slope below 7.0%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".