Influence of young black spruce plantations on moose winter distribution
Bibliographic record
Abstract
Abstract Logging in the boreal forest may benefit moose by increasing food availability. However, the influence of tree plantations on moose behavior, especially on moose spatial ecology, is poorly understood. We assessed the impacts of black spruce plantations on moose winter distribution at a landscape scale in the Bas‐Saint‐Laurent region (Québec, Canada). We used winter aerial surveys to examine relationships among plantation characteristics and other habitat variables known to affect moose distribution. The total area of plantations positively influenced moose abundance, but highly aggregated plantations resulted in fewer moose. Moose abundance was also positively associated with food availability and the density of edges between stands providing cover and stands offering high food availability, but moose abundance was negatively associated with road density. Although plantation characteristics were less influential than habitat variables related to foraging and predator avoidance, we demonstrate that the area of black spruce plantations and their configuration should be considered in moose management. We conclude that an integrated management strategy is needed to find a balance between overdeveloped road networks (needed to join homogeneously distributed plantations) and agglomerated plantations in order to mitigate impacts on moose winter distribution. © 2012 The Wildlife Society.
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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".