Detecting between-individual differences in hind-foot length in populations of wild mammals
Bibliographic record
Abstract
Hind-foot length is a widely used index of skeletal size in population ecology. The accuracy of hind-foot measurements, however, has not been estimated. We quantified measurement error in adult hind-foot length in yellow-bellied marmots (Marmota flaviventris (Audubon and Bachman, 1841)), mountain goats (Oreamnos americanus (de Blainville, 1816)), and bighorn sheep (Ovis canadensis Shaw, 1804) from long-term capture–recapture studies. Fitting a linear mixed effect model for each species separately, we found that hind-foot length was significantly repeatable in the three species, but repeatability was low, ranging from 0.30 to 0.47. Measurement error explained 53%–66% of the variance in foot length. Differences of 6, 13, and 27 mm would be indistinguishable from measurement error for marmots, goats, and sheep, respectively. At least 4–6 measures per individual were needed to detect variation in foot length between individuals of a population using a mixed effect model. Researchers should strive to limit measurement errors because inaccurate measures may obscure important biological patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".