Evaluation of acaricide treatments to experimentally reduce winter tick load on moose
Bibliographic record
Abstract
Abstract Quantifying the consequences of winter ticks ( Dermacentor albipictus ) on the body condition and life‐history traits of moose ( Alces alces ) is a challenge due to several confounding factors. We experimentally reduced tick load on moose calves by testing the effectiveness of 2 acaricide treatments: one using topical permethrin (5%) alone and the other a combination of a more concentrated topical permethrin (44%) and orally administered fluralaner (25 mg/kg). We evaluated changes in tick load, body mass, hematocrit, and hair loss severity and occurrence, from recaptured or resighted moose calves over winter in Québec and New Brunswick, Canada. Nearly all untreated moose (94%, n = 41) experienced hair loss compared to calves that received the combination of permethrin (44%) and fluralaner (41%, n = 37). Of treated moose that exhibited hair loss, only 22% had more than 5% damage and some already had hair loss at capture. Capturing moose later likely increased the probability of observing hair loss when resighting treated moose, although hair loss essentially remained lower for treated calves than for untreated calves. In untreated moose, tick load at capture tended to drive hair loss, but calendar date mostly drove hair loss severity, especially during April. There was no clear effect of topical permethrin (5%) on tick load, body mass, and hematocrit. Body condition simply decreased from January captures to spring recaptures, regardless of treatment. Our results suggested that combining permethrin (44%) and fluralaner effectively reduced tick load based on hair loss severity and occurrence. We cannot, however, disentangle the individual effects of permethrin (44%) and fluralaner. We discuss research implications and considerations of using such a treatment for reducing winter tick load.
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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 | 0.001 | 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".