Rates of maximum food intake in young northern fur seals (<i>Callorhinus ursinus</i>) and the seasonal effects of food intake on body growth
Bibliographic record
Abstract
Accurate estimates of food intake and its subsequent effect on growth are required to understand the interaction between an animal’s physiology and its biotic environment. We determined how food intake and growth of six young northern fur seals ( Callorhinus ursinus (L., 1758)) responded seasonally to changes in food availability. Animals were given unrestricted access to prey for 8 h·day–1on either consecutive days or on alternate days only. We found animals offered ad libitum food on consecutive days substantially increased their food intake over normal “training” levels. However, animals that fasted on alternate days were unable to compensate by further increasing their levels of consumption on subsequent feeding days. Absolute levels of food intake were highly consistent during winter and summer trials (2.7–2.9 kg·day–1), but seasonal differences in body mass meant that fur seals consumed more food relative to their body mass in summer (~27%) than in winter (~20%). Despite significant increases in absolute food intake during both seasons, the fur seals did not appear to efficiently convert this additional energy into mass growth, particularly in the winter. These seasonal differences in conversion efficiencies and estimates of maximum intake rates can be used to generate physiologically realistic predictions about the effect of changes in food availability on an individual fur seal, as well as the consequences for an entire population.
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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 | 0.001 | 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".