Variation in body condition of migratory caribou at calving and weaning: Which measures should we use?
Bibliographic record
Abstract
Monitoring the body condition of ungulates is often considered an efficient way to assess habitat quality. It is therefore essential to select adequate measures to describe individual body condition. Because there is no consensus on which measurement(s) can best describe individual variability in body condition, field biologists often measure several variables, increasing processing time. From 2007 to 2009, we assessed body condition of female—calf pairs in 2 herds of migratory caribou in Northern Quebec/Labrador, Canada, using multiple measurements of size, mass, and fat depth. We sought to identify, using multivariate analysis, which measurement(s) had the greatest influence on a composite measure of body condition of females and calves at calving and weaning. Our results indicate that adult females are best described with a body bulkiness index opposing heavy and long/round-bodied females with high body protein reserves to light and short/slender-bodied females with low body protein reserves. At weaning, adult females can also be differentiated by a body fat index opposing fat to lean females. Calf body condition is best described by mass at birth and by a combination of mass and size measurements at weaning, opposing heavy and tall individuals with high protein reserves to light and short ones with low protein reserves. Overall, body mass appears to be the measurement that best describes individual variability in body condition of females and calves at calving and weaning. Our systematic comparison of body condition measurements will provide field biologists with guidance for future data collection.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".