Edge influence on forest structure in large forest remnants, cutblock separators, and riparian buffers in managed black spruce forests
Bibliographic record
Abstract
:Remnants of old forests left on the landscape following forest harvesting, especially corridors, provide benefits of connectivity and facilitation of movement or dispersal, which may be hindered by the presence of edges. Our objective was to determine the extent of edge influence on forest structure in these forest remnants in black spruce boreal forest. We sampled canopy cover and the density of trees, snags, and logs along clearcut edge–forest gradients in large forest patches, cutblock separators, and riparian buffers. The distance-of-edge influence was determined by comparing values at different distances from the edge to values in interior forest using randomization tests. Forest remnants had lower live tree density and canopy cover and higher mortality and windthrow than interior forest. Distance-of-edge influence on forest structure extended 10–30 m from the edge, and was slightly more extensive into cutblock separators where two edges are in close proximity, but was less extensive in riparian buffers, possibly due to the presence of stable internal edges near the stream. Because of edge influence, structure near the edges of forest remnants and across narrow corridors is modified; wider corridors would be required to provide a core habitat of interior forest conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".