Extensive forest management contributes to maintain suitable habitat characteristics for the endangered Atlantic-Gaspésie caribou
Bibliographic record
Abstract
Extensive forest management aims at minimizing differences between managed and natural forests and at contributing to the conservation of endangered species such as the Atlantic-Gaspésie caribou. The decline of this isolated population was exacerbated by intensive forest practices, as the over-representation of regenerating forests supports high densities of bears and coyotes. These predators select such stands for the high availability of berries and browse suitable to alternative prey, especially moose. Our objective was to verify whether extensive treatments can provide suitable habitat characteristics for caribou. We compared the impacts of different intensive and extensive treatments on habitat attributes known to be selected by caribou, moose, and their predators. We sampled 291 sites in seven treatments and in mature coniferous forests (as the control). A partial canonical correspondence analysis highlighted which treatments maintain habitat attributes that are comparable with those found in mature forests, including some characteristics suitable for caribou such as a substantial biomass of arboreal lichen and a lower availability of resources for predators. Although being more suitable than the three intensive treatments tested, none of the four extensive treatments we studied provided similar habitat conditions to mature forest. Favouring extensive treatments could nevertheless be a relevant conservation compromise at the forest stand level, but their utility remains uncertain under the maximum sustainable yield paradigm as they impact a larger area.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".