Logging-induced variations in dissolved organic carbon affect yellow perch (<i>Perca flavescens</i>) recruitment in Canadian Shield lakes
Bibliographic record
Abstract
There is increasing interest in the effects of allochthonous carbon on lake food webs. By temporarily increasing levels of dissolved organic carbon (DOC) in lakes, logging can help us understand how carbon from the watershed could affect lake biota. The goals of this study were to determine whether (i) logging has a significant effect on the abundance of young-of-the-year (YOY) yellow perch (Perca flavescens) in Canadian Shield lakes and (ii) any changes in yellow perch recruitment could be related to increases in nutrients (N and P) and (or) DOC following logging. To do this, we examined 22 Canadian Shield lakes: the watersheds of 13 were not impacted, while 9 underwent logging (1%–78% of the watershed area). We found that the relative abundance of YOY yellow perch increased after logging in proportion to the ratio between the area of the logged watershed and the lake volume. We show that this effect is likely explained by an increase in DOC following logging. This might be related to (i) an increase in secondary productivity due to a positive effect of terrestrial carbon on the microbial loop and (or) (ii) an increased hatching success and (or) larval survival due to a greater protection from UV radiation by DOC.
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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 | 0.001 |
| 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 | 0.000 |
| 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".