Variation in Muskox (Ovibos moschatus) Guard Hair Growth Rates: Implications for Measuring Chronological Biomarkers
Bibliographic record
Abstract
Segmental analyses of hair may be useful for measuring biomarkers over several seasons to years from a single sample. To attribute hair segments to specific time periods, a known chronological marker, or a hair growth rate, is needed. We examined guard hair growth rates of captive muskoxen (Ovibos moschatus) in Fairbanks, Alaska, USA. We sought to determine if a general growth rate could be applied across muskox populations, thus facilitating the use of segmental analyses for various biomarkers. We used archived samples from 16 muskoxen that had guard hairs sampled at six, 14, and 30 wk after shaving. We measured the lengths of 10 guard hairs per sample, calculated weekly and annual growth rates, and then fitted linear mixed-effects models to assess the effect of different covariates on hair growth rate. The period in which hair had been grown had a significant effect (P<0.05) on growth rate. Extrapolated annual hair growth rates were 277±40 mm/yr (weeks 0-6), 248±47 mm/yr (weeks 7-14), and 165±36 mm/yr (weeks 15-30), with an overall average rate of 210±14 mm/yr. These rates were significantly faster than those of free-ranging Greenland muskoxen-78 mm/yr as measured by stable isotope analyses-and varied intra-annually. This suggests that a universal growth rate cannot be generalized across muskox populations and time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".