Alternative silvicultural practices in irregular boreal forests: response of beetle assemblages
Bibliographic record
Abstract
In the process of implementing sustainable management in the eastern Canadian boreal forest, we tested two selection cutting methods and compared them with two widely used practices in the boreal forest: clearcutting with protection of the advanced growth and soils and irregular shelterwood cutting leaving small merchantable stems. We used old-growth irregular stands as references in comparing the impact of these silvicultural treatments on the diversity and abundance of beetles. Three groups were targeted: saproxylic flying beetles, epigaeic saproxylic beetles and epigaeic non-saproxylic beetles. A sampling design including 320 pitfall traps and 80 multidirectional flight-interception traps was deployed in 2007. A total of 26 906 beetles was captured including 407 taxa distributed among 52 families. We found that clearcutting with protection of the advanced growth and soils and irregular shelterwood cutting leaving small merchantable stems had a greater impact on beetle communities than both selection cuttings. Canopy opening as well as the presence of snags and downed woody debris appear as important attributes for several saproxylic and non-saproxylic species. Beetle communities in selection cuttings remained more similar to those found in controls; these silvicultural treatments are new tools to implement ecosystemic and sustainable management in irregular boreal forests.
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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 | 0.000 | 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) | 0.001 | 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".