Foraging effort, food intake and lactation performance depend on maternal mass in a small phocid seal
Bibliographic record
Abstract
Summary Female mammals increase energy expenditure during lactation to support the high cost of milk production. The extent to which lactation in a small phocid species, the Harbour Seal Phoca vitulina L., was fuelled by food vs body stores, how this allocation varied with maternal body mass and the consequences of maternal expenditure on offspring growth were studied. The proportional body composition of 30 females was independent of initial postpartum body mass, but larger females had absolutely more stored energy than smaller ones. Females lost 32% of postpartum body mass and 62% of body energy by late lactation; 97% of energy loss was derived from body fat. Percentage loss of body energy was independent of initial body mass, indicating that females limit their allocation of body stores to offspring by expending a constant proportion of stores rather than a constant amount. Females spent more time diving and individual dives were deeper and longer as lactation progressed. By late lactation, these characteristics of diving were inversely proportional with initial postpartum mass. During early lactation, female expenditures were covered mainly by a reduction in body energy stores. By late lactation, food intake increased six‐fold but the extent of this increase varied inversely with postpartum mass. Pup growth rate and weaning mass were positively related to postpartum mass and total daily energy expenditure of females, but were independent of the source of energy used by females during lactation. Pups of heavy females had higher survival than pups of light females. Our results support the hypothesis that maternal body mass is an important determinant of lactation strategies in pinnipeds.
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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".