Are warbles and bots related to reproductive status in West Greenland caribou?
Bibliographic record
Abstract
In March-April 2008-09, using CARMA protocols, 81 cows and 16 calves were collected in West Greenland from two caribou populations; Akia-Maniitsoq (AM) and Kangerlussuaq-Sisimiut (KS). In both populations, warble larvae numbers were highest in calves and higher in non-pregnant than pregnant cows. Nose bots showed no relationship with pregnancy or lactation; KS calves had higher nose bot loads than cows, a pattern not observed in AM. Pregnant cows had more rump fat than non-pregnant cows. KS cows lacking rump fat entirely had the highest warble burdens. We observed lactating pregnant cows with moderate larval burdens. Projected energy cost of the heaviest observed combined larvae burdens was equivalent to 2-5 days basal metabolic rate (BMR) for a cow, and 7-12 days BMR for a calf. Foregone fattening in adult cows with average burdens was 0.2 to 0.5 kg, but almost doubled with the heaviest infestations to 0.4 and 0.8 kg. Average burdens in calves resulted in forgone fattening of about 0.5 kg, with peak costs equivalent to 0.7 and 1.1 kg fat for AM and KS calves respectively. Although modest, these projected energy costs of hosting larvae for cows support the negative relationship between rump fat and larvae burden. For calves, hosting high burdens of warble larvae could affect winter survival, specifically those weaned normally in October or in early winter. Harmful effects of oestrid larvae burdens may remain subtle but clearly cumulative in relation to seasonal forage availability and incidence of other parasites.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".