What makes elk tick: winter tick (<i>Dermacentor albipictus</i>) grooming behavior in wild elk (<i>Cervus canadensis</i>)
Bibliographic record
Abstract
Abstract Ungulates groom to remove ectoparasites but grooming may interfere with foraging, vigilance, and rumination, and it is possible that these effects differ among migratory tactics due to differences in parasite infestations. We compared the effects of grooming for winter ticks (Dermacentor albipictus) on winter foraging behavior by migrating and resident elk (Cervus canadensis) in the partially migratory population at the Ya Ha Tinda, adjacent to Banff National Park, Canada. We used hair loss on the dorsal shoulder area (“withers”) measured from photographic images as an index of tick infestation of individual elk. We conducted 594 focal observations on 48 radio-collared and 18 uncollared individuals that were uniquely identifiable from ear-tags (N = 66) in 2019 to assess whether grooming for ticks in winter reduced time spent foraging, ruminating, or being vigilant. Because rubbing or hair loss from radio-collars may influence tick infestations and behavior, we controlled for whether elk were collared or uncollared in our analyses. Neck hair loss was 3−5% greater in collared elk than uncollared elk, but neither withers hair loss nor time spent grooming differed. Grooming occurred during 42% of the observations but grooming comprised only ~1% of observation time. Nevertheless, 40% of all grooming was observed during resting, and grooming interrupted vigilance behavior ~8 times more than foraging. We found no differences among elk following different migratory tactics in time spent grooming or in other behaviors, but one of the two groups of migrant elk had higher withers hair loss. Our results suggest winter ticks may have slight effects on elk relative to other ungulates, particularly moose (Alces alces), in North America.
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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) | 0.001 | 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".