Effects of forest harvesting on leaf litter dynamics across the aquatic-terrestrial ecotone of boreal lakes
Bibliographic record
Abstract
The flux of nutrients and biomass associated with leaf litter across the terrestrial-aquatic interface of a suite of small boreal lakes in northeastern Ontario was investigated in relation to the impacts of forest harvesting. The oligotrophic lakes were categorized as 'clear' or 'boggy' based on water chemistry. Litter biomass varied little across lakes and years, but was typically an order of magnitude less for boggy lakes. Pre and post-harvest comparisons indicated little evidence for impacts of forest harvesting on litter biomass deposition in catchments with up to 19% clear-cut harvesting. Similarly, few significant differences in nutrient fluxes were detected between lakes or years. The relative concentrations of elements were ranked as N>Ca>K>Mg>P>Na. The highest concentrations of N were found in alder ('Alnus incana spp. Rugosa') leaves, which represented approximately 30% of all litter collected. Shoreline alder stands may therefore be an important source of N to nearshore aquatic communities. Harvesting practices impacting alder may be detrimental to littoral shoreline communities.
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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 | 0.000 |
| 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".