Sequential analysis of δ <sup>15</sup> N in guard hair suggests late gestation is the most critical period for muskox calf recruitment
Bibliographic record
Abstract
Rationale Analysis of stable isotopes in tissue and excreta may provide information about animal diets and their nutritional state. As body condition may have a major influence on reproduction, linking stable isotope values to animal demographic rates may help unravel the drivers behind animal population dynamics. Methods We performed sequential analysis of δ 15 N values in guard hair from 21 muskoxen ( Ovibos moschatus ) from Zackenberg in high arctic Greenland. We were able to reconstruct the dietary history for the population over a 5‐year period with contrasting environmental conditions. We examined the linkage between guard hair δ 15 N values in 12 three‐month periods and muskox calf recruitment to detect critical periods for muskox reproduction. Finally, we conducted similar analyses of the correlation between environmental conditions (snow depth and air temperature) and calf recruitment. Results δ 15 N values exhibited a clear seasonal pattern with high levels in summer and low levels in winter. However, large inter‐annual variation was found in winter values, suggesting varying levels of catabolism depending on snow conditions. In particular δ 15 N values during January–March were linked to muskox recruitment rates, with higher values coinciding with lower calf recruitment. δ 15 N values were a better predictor of muskox recruitment rates than environmental conditions. Conclusions Although environmental conditions may ultimately determine the dietary δ 15 N signal in muskox guard hairs, muskox calf recruitment was more strongly correlated with δ 15 N values than ambient snow and temperature. The period January–March, corresponding to late gestation, appears particularly critical for muskox reproduction.
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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 | 0.000 | 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".