Heat increment of feeding in the common bottlenose dolphin ( <i>Tursiops truncatus</i> ) contributes moderately to field metabolic rate estimates
Bibliographic record
Abstract
Digestion elevates metabolism through the heat increment of feeding (HIF) - the energy expended on mechanical and biochemical processes after eating. Quantifying this cost is essential for bioenergetic models that predict energy flow and prey requirements in populations. Using breath-by-breath respirometry, we measured oxygen consumption (V̇O2) in eight common bottlenose dolphins (Tursiops truncatus) before and after feeding standardized meals (1659-2658 kcal of capelin and herring). Metabolic rate rose by ∼37% above resting levels, peaking 60 min after feeding before returning to baseline within 2 h. When scaled across the day, digestion increased daily metabolic needs by ∼8.2% of basal metabolism, similar to values reported for Steller sea lions (Eumetopias jubatus) and harbour seals (Phoca vitulina), where HIF contributes 4-10% of daily energy expenditure. This study provides the first multi-individual estimate of HIF in dolphins and suggests that the energetic cost of digestion is a moderate contribution to overall daily metabolism, refining energetic models and improving prey requirement estimates for cetaceans in the wild.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".