Windthrow in riparian buffers affects the water quality of freshwater ecosystems in the eastern Canadian boreal forest
Bibliographic record
Abstract
Despite the wide application of riparian buffers in the managed boreal forest, their long-term effectiveness as freshwater protection tools remains unknown. Here, we evaluate windthrow incidence in riparian buffers in the eastern Canadian boreal forest and determine the effect of windthrow on the water quality index of the adjacent freshwater ecosystems. We studied 40 sites-20 riparian buffers, aged 10 to 20 years after harvesting and 20 control sites within intact riparian environments-distributed among clay and sandy (esker) soils and black spruce (Picea mariana) and jack pine (Pinus banksiana) stands. We observed more windthrow in the harvested stands (36%) relative to the control sites (16%), regardless of substrate and species. We determined that the most important factors explaining windthrow were exposition, harvesting, aquatic environment size, and stand characteristics. These factors drive wind exposure, speed, and force, which determine post-harvest windthrow risk. Furthermore, windthrow negatively affected the water quality index of the adjacent aquatic systems, i.e., greater windthrow decreased the protective effect of the riparian buffer. We recommend increasing the use of partial harvest near riparian environments and adapting riparian buffers to site conditions to ensure the long-term protection of adjacent freshwater ecosystems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".