Downed wood dynamics in the riparian and littoral zone of small lakes in tolerant hardwood forests
Bibliographic record
Abstract
Large wood (LW) is an important structural feature in forested lake ecosystems, but little is known about the production of LW in riparian forests and its movement across the forest–lake ecotone, both of which we describe here for eight small (<30 ha) lakes in Ontario, Canada. The creation of snags through live-tree death varied by species, ranging from 0.24% year−1 for eastern white cedar to 5.60% year−1 for balsam fir. Snag breakage was best described as a function of snag height and snag fall described as a function of tree species, decay stage, and diameter. Long LW pieces and pieces generated close to the lake were more likely to be deposited at the shoreline. The density of LW at the shoreline was 125–550 pieces·km−1, and density and volume of LW were positively related to riparian slope. LW was a stable resource at the shoreline, with a recruitment rate about equal to the loss rate and average movement along the shoreline of 2.89 cm·year−1. LW volumes at the shoreline and in the littoral zone of the lake were positively related to lakebed slope. LW could reside in the littoral zone for centuries, providing physical structure for multiple generations of aquatic organisms in these small lakes. Our results indicate that critical habitat for fish that need aquatic LW extends at least 5 m into riparian forest and that removal of trees within 5 m of the forest edge would reduce LW input to the littoral zone.
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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 | 0.000 |
| 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".