Stable isotopic signatures in modern wood bison (<i>Bison bison athabascae</i>) hairs as telltale biomarkers of nutritional stress
Bibliographic record
Abstract
Assessing the challenges faced by wildlife populations is key to providing effective management but is problematic when dealing with populations in remote locations. Analyses of the stable carbon and nitrogen isotope composition (expressed as δ13C and δ15N values) of sequentially grown tissues, such as hairs, can be used to track changes in the eco-physiology of organisms. We generated δ13C and δ15N values from sequentially sampled (n = 465) hairs taken from wood bison (Bison bison athabascae Rhoads, 1898) (n = 27). Samples were taken from individuals prior to and after their release from captivity into the lower Innoko–Yukon river area of Alaska in 2015. Twenty months after release, individuals had a distinct seasonal pattern in δ13C values. Hairs from individuals that experienced food scarcity or long-distance movement were sampled as case studies. Nutritional stress in these cases lead to a rise in δ15N values and a decrease in δ13C values. Applications of δ13C and δ15N analyses of bison tail hairs could provide wildlife managers a valuable and minimally invasive tool to better understand bison seasonal metabolic status and determine the historical health and behavior of living and dead individuals.
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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 | 0.001 | 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".