MOOSE ESCAPE BEHAVIOUR IN AREAS OF HIGH HUNTING PRESSURE
Bibliographic record
Abstract
3 e-mail: john.ball@szooek.slu.se ABSTRACT: Although hunters cause more than 80% of moose mortality in some geographic areas, quantitative studies of how moose attempt to escape humans are surprisingly rare. We experimen- tally disturbed radio-collared moose of known age and of both sexes to study escape behaviour from humans. We found that larger groups of moose made fewer stops between being disturbed and settling down, and that larger groups exhibited a longer path length before quieting. We detected no significant effect of age (a potential measure of survival rate) on escape behaviour. The escape path of males was significantly longer than females even though the linear distance from the site of disturbance to the location where the moose settled down was not significantly different between the sexes. Overall, the escape path of males from the site of disturbance to where they settled down was significantly more tortuous than that of females. Although males are the preferred prey of hunters, the differences in escape behaviour between the sexes also may contribute to why males are more frequently killed by hunters. Thus, in areas with heavy hunting pressure, hunters may be acting as a selective force that favours animals that immediately run away after disturbance by humans. Finally, published evaluations of the use of hunter observations to index moose populations have often reported that considering the size of a hunting group is necessary to improve the accuracy of those data; our analysis suggests an explanation - differences in escape behaviour between the sexes. ALCES VOL. 40: 123-131 (2004)
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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 | 0.000 | 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) | 0.002 | 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".