Wildlife-mitigated precommercial thinning maintains the abundance of fruit shrubs in a boreal forest
Bibliographic record
Abstract
In boreal forests, fruits are an abundant resource in late summer and benefit many wildlife species. Fruits are mainly found in early successional stands, which are often subject to precommercial thinning designed to increase diameter growth of residual trees and manage stand species composition. Concerns about the consequences of precommercial thinning on wildlife have led to various methods of precommercial thinning with mitigation for wildlife. In summers 2007 and 2008, we examined the impact of wildlife-mitigated thinning on fruit shrub abundance and distribution at the Forêt Montmorency, Quebec. The abundance of fruit shrubs of all species except Amelanchier was similar in thinned and control stands but was highly variable among individual stands. Amelanchier shrubs appeared to benefit from thinning, especially 10 to 20 years after clearcutting. Fruit shrubs were highly clustered within early successional stands, but less so after thinning. We conclude that wildlife-mitigated precommercial thinning does not reduce access to fruits for birds and other frugivores and may even facilitate it in eastern Canadian boreal forests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".