Forest structure and composition at young fire and cut edges in black spruce boreal forest
Bibliographic record
Abstract
We compared structure and composition at forest edges created by wildfire and clear-cutting in black spruce (Picea mariana (Mill.) BSP) dominated boreal forest in northwestern Quebec. Forest structure and plant species composition were sampled along transects perpendicular to eight 3- to 4-year-old fire edges and eight 2- to 5-year-old cut edges. Significance of edge influence was assessed by comparing mean values at different distances from the edge to the range of variation in interior forest. The influence of clearcut edges was minimal, generally extending only 5 m from the edge, and included greater log density and different species composition, compared with interior forest. At fire edges, prominent responses to edge creation included increased snag density and lower moss cover, compared with interior forest, extending up to 40 m into the forest. This initial structural change was likely due to partial burning extending into the forest. Overall, fire edges had more snags and a different species composition than cut edges. Our hypothesis that edge influence is more extensive at fire edges than at cut edges was supported for overstory and understory structure, but not for species composition. We suggest that there is a need for management to consider the cumulative effect of the loss of fire edges on the landscape.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".