Wolf predation on moose - a case study using hunter observations.
Bibliographic record
Abstract
We studied predation by colonizing wolves on a high density and highly productive moose ( Alces alces) population in south-eastern Norway (about 1.5 moose and 0.01 wolves per km 2 in winter). As indices to population changes, we used hunter observations. Over the summer, the wolf pack utilized about one tenth of their total territory (530 km 2 ), with the den area as the centre of activity. Of the main prey taken (moose, roe deer, and beaver), moose calves contributed 61% of the biomass ingested by wolves in summer. Hunting statistics and hunters' observations of moose showed no changes for the territory as a whole after wolves settled there in 1998. However, in the den areas (60 - 80 km 2 ) the number of calves per cow and the total number of moose seen per hunter-day significantly decreased during the year of wolf reproduction. The following year, though, both indices increased again. We speculate that some of the lack of overall effects might be due to increased fecundity in cows that lost their calf. As the wolves changed their den from year to year, den areas were spatially spread over time. The pressure from wolf predation will differ between cohorts in the same area, and landowners should adjust their hunting quotas accordingly. ALCES VOL. 39: 263-272 (2003)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".