Lactation performance in polar bears is associated with fasting time and energetic state
Bibliographic record
Abstract
Females must continually make resource allocation decisions because of fitness trade-offs between self-maintenance and investment in current offspring, yet factors underpinning these decisions are unresolved. Polar bears Ursus maritimus face considerable allocation challenges when seasonal sea-ice melt precludes access to prey for several months, and females rely solely on energy stores to cover their own energetic needs and provision offspring. We tested how female polar bears regulate lactation during onshore fasting (i.e. capital breeding) and determined the consequences of moderated lactation for females and cubs. Overall, milk energy declined, and lactation was more likely to cease with longer time fasting. Lactation was partially mediated by maternal energetic state and depended on litter characteristics. Milk energy declined more sharply with fasting time (~2.6 times more strongly) in females with 2 offspring compared to those with 1. Females with cubs-of-the-year produced higher energy milk than those with yearlings, and their milk energy also increased more strongly with maternal energy density. Milk energy declines benefited females via reduced depletion of maternal energy reserves, but cub growth decreased. Altered lactation investment likely has consequences for both female survival and the fate of offspring, which could scale up to influence population dynamics. Given that Arctic warming means polar bears across much of their range will experience longer periods without access to primary prey, our results underscore how lactation will likely become increasingly compromised.
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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".