Comparing body condition of moose (<i>Alces alces</i>) selected by wolves (<i>Canis lupus</i>) and human hunters: consequences for the extent of compensatory mortality
Bibliographic record
Abstract
Predators commonly select prey of inferior quality compared with the average animals found in the population. Consequently, predation may often be compensatory to other sources of mortality. We tested whether wolves ( Canis lupus L., 1758) in Scandinavia selected moose in poor body condition by comparing mandibular marrow fat (MMF) of wolf-killed moose ( Alces alces (L., 1758)) with harvested moose. Model selection analyses indicated that MMF levels were lower for wolf-killed moose compared with harvested moose, but an unbalanced sample design between years for the two causes of death (wolves and harvest) may have confounded a clear interpretation of the results. Nevertheless, corrected MMF levels (to 1 April) showed that a significantly (p = 0.006) higher proportion of the wolf-killed calves (18.5%) were below the level often assumed to reflect acute malnutrition (<20% MMF) compared with harvested moose calves (3.3%). For wolf-killed yearlings and adult females, 5.6% and 8.3%, respectively, had MMF below this level compared with 0% for harvested individuals. As a result, 15.1% of the total number of wolf-killed moose and 1.6% of the harvested moose during winter appear to be compensatory to mortality from starvation. Differences in body condition, and thus levels of compensatory mortality, found between wolf-killed and harvested moose in this study may reflect a stronger selection for individuals in poor body condition by wolves.
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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 | 0.000 |
| 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".