Multiple nutritional currencies shape pregnancy in a large herbivore
Bibliographic record
Abstract
Nutritional condition embodies environmental conditions experienced by animals with survival and reproductive consequences. Body fat is often associated with ungulate fecundity; however, other nutritional currencies may limit fecundity. Using data from 129 moose (Alces alces (Linnaeus, 1758)) monitored over 429 moose-years, we examined the limiting role of multiple nutritional currencies on pregnancy rates while concurrently assessing the influence of age and prior reproduction. Females tended to be pregnant in successive years, suggesting differences in individual or habitat quality. However, probability of pregnancy declined with survival of calves from prior litters, indicating a reproductive cost to rearing offspring. Pregnancy was positively associated with serum iron (Fe), body fat, body mass, and serum protein. The best model predicting pregnancy included serum Fe, body fat, and age class, with serum Fe being the strongest single predictor. Moose with Fe concentrations considered deficient in cattle (Bos taurus Linnaeus, 1758) had pregnancy rates of 33%–35%, whereas 89%–91% of individuals with sufficient Fe were pregnant. We subsequently evaluated hypotheses concerning factors potentially limiting Fe concentrations, including Fe-deficient diet, chronic infection, parasitism, and malnutrition. The best supported hypothesis was energy and protein malnutrition constrained Fe stores. We conclude that subclinical anemia due to nutritional constraints can limit or indicate limits in moose fecundity.
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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 | 0.001 |
| 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".