Establishment of natural regeneration under severe browsing pressure from white-tailed deer after group seed-tree cutting with scarification on Anticosti Island
Bibliographic record
Abstract
The use of large clearcuts with protection of advance regeneration is inappropriate for regeneration of balsam fir ( Abies balsamea (L.) Mill.) under severe browsing from white-tailed deer ( Odocoileus virginianus (Zimmermann)). However, little is known about the effects of tree-retention methods along with scarification on the establishment of balsam fir in a context of severe herbivory. Consequently, we hypothesized that seed-tree-group cutting in conjunction with soil scarification creates favourable conditions for balsam fir regeneration. In 1998 and 1999 we set up three circular forest groups of different sizes (40, 60, and 80 m in diameter) surrounded by three different intensities of scarification (no treatment, single pass, double pass) in two balsam fir stands on Anticosti Island, Quebec, Canada. Moreover, we used a network of fenced and unfenced regeneration plots to evaluate the impact of white-tailed deer browsing. The results showed that up to 7 years after harvest, seed-tree-group cutting did not improve the establishment of conifer species. Scarification had a negative impact on the abundance of conifer species, whereas white birch ( Betula papyrifera Marshall) density was significantly higher in areas scarified twice than in unscarified areas. Overall, the development of palatable tree species appears unlikely at deer densities >20 individuals/km2.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".