Weaning Mass Affects Changes in Body Composition and Food Intake in Harbour Seal Pups during the First Month of Independence
Bibliographic record
Abstract
In phocid seals, the transition to nutritional independence is abrupt, with females abandoning their offspring after weaning and returning to sea. We hypothesized that body size at weaning may play an important role in the nature of this transition. We studied the changes in body composition and water flux of newly weaning harbour seals over the first 4-6 wk postweaning. Thirty-three pups were dosed with deuterium oxide to estimate total body water (TBW) and a subset of 24 was dosed twice to estimate changes in body composition and water flux. All pups lost body mass over the study period, but TBW increased during the period of mass loss, indicating continued lean tissue growth. Combined data from this and our early study indicated that heavy (>median mass) pups were relatively fatter (41.0% vs. 37.1%) and had significantly greater total body energy at weaning than did light (< or = median mass) pups. Percentage TBW declined linearly over time in light pups but was constant in heavy pups for the first 19 d postweaning and then declined linearly. Both the temporal pattern and composition of mass loss differed between light and heavy pups. Estimated food intake increased in the second 2 wk of study compared to the first 2 wk, in both light and heavy pups, reflecting increased foraging success but at levels still insufficient to meet daily expenditures of most 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 | 0.000 | 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".