Winter presence of moose in clear-cut black spruce landscapes: related to spatial pattern or to vegetation?
Bibliographic record
Abstract
Winter aerial surveys of moose (Alces alces) were completed on 14 landscapes (10–256 km2 ) formed of aggregated black spruce (Picea mariana) clear-cuts logged 3–9 years ago in southcentral Quebec. Moose were present in 8 landscapes (11 yards) and had a mean density of 0.20 moose/10 km2, which was 50% of the density observed in the same hunting zone with a similar forest composition. Based on previous work, effects of variability in hunting pressure and time since cutting were assumed not to influence distribution and abundance of moose. Browse density did not increase with age of cuts. Moose density was not related to the size of the clear-cut landscapes or the proportion of residual forest (18–40%) within each landscape (P = 0.14). Moose yards were not located close to uncut forest surrounding the landscapes and did not have a greater proportion of residual forest than clear-cut landscapes. Moose yards had a denser shrub layer and more browse available than random sites selected in the same landscapes. The presence of moose in large clearcut black spruce landscapes is related to vegetation characteristics and not the spatial pattern of the forest. The authors propose two strategies to maintain moose populations and moose hunting activity in this type of forest after harvesting.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".