Mechanics, hydrodynamics and energetics of blue whale lunge feeding: efficiency dependence on krill density
Bibliographic record
Abstract
Lunge feeding by rorqual whales (Balaenopteridae) is associated with a high energetic cost that decreases diving capacity, thereby limiting access to dense prey patches at depth. Despite this cost, rorquals exhibit high rates of lipid deposition and extremely large maximum body size. To address this paradox, we integrated kinematic data from digital tags with unsteady hydrodynamic models to estimate the energy budget for lunges and foraging dives of blue whales (Balaenoptera musculus), the largest rorqual and living mammal. Our analysis suggests that, despite the large amount of mechanical work required to lunge feed, a large amount of prey and, therefore, energy is obtained during engulfment. Furthermore, we suggest that foraging efficiency for blue whales is significantly higher than for other marine mammals by nearly an order of magnitude, but only if lunges target extremely high densities of krill. The high predicted efficiency is attributed to the enhanced engulfment capacity, rapid filter rate and low mass-specific metabolic rate associated with large body size in blue whales. These results highlight the importance of high prey density, regardless of prey patch depth, for efficient bulk filter feeding in baleen whales and may explain some diel changes in foraging behavior in rorqual whales.
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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".