Rorqual Lunge-Feeding Energetics Near and Away from the Kinematic Threshold of Optimal Efficiency
Bibliographic record
Abstract
Humpback and blue whales are large baleen-bearing cetaceans, which use a unique prey-acquisition strategy-lunge feeding-to engulf entire patches of large plankton or schools of forage fish and the water in which they are embedded. Dynamically, and while foraging on krill, lunge-feeding incurs metabolic expenditures estimated at up to 20.0 MJ. Because of prey abundance and its capture in bulk, lunge feeding is carried out at high acquired-to-expended energy ratios of up to 30 at the largest body sizes (∼27 m). We use bio-logging tag data and the work-energy theorem to show that when krill-feeding at depth while using a wide range of prey approach swimming speeds (2-5 m/s), rorquals generate significant and widely varying metabolic power output during engulfment, typically ranging from 10 to 50 times the basal metabolic rate of land mammals. At equal prey field density, such output variations lower their feeding efficiency two- to three-fold at high foraging speeds, thereby allowing slow and smaller rorquals to feed more efficiently than fast and larger rorquals. The analysis also shows how the slowest speeds of harvest so far measured may be connected to the biomechanics of the buccal cavity and the prey's ability to collectively avoid engulfment. Such minimal speeds are important as they generate the most efficient lunges. Sommaire Les rorquals à bosse et rorquals bleus sont des baleines à fanons qui utilisent une technique d'alimentation unique impliquant une approche avec élan pour engouffrer de larges quantités de plancton et bancs de petits poissons, ainsi que la masse d'eau dans laquelle ces proies sont situés. Du point de vue de la dynamique, et durant l'approche et engouffrement de krill, leurs dépenses énergétiques sont estimées jusqu'à 20.0 MJ. À cause de l'abondance de leurs proies et capture en masse, cette technique d'alimentation est effectuée à des rapports d'efficacité énergétique (acquise -versus- dépensée) estimés aux environs de 30 dans le cas des plus grandes baleines (27 m). Nous utilisons les données recueillies par des capteurs de bio-enregistrement ainsi que le théorème reliant l'énergie à l'effort pour démontrer comment les rorquals s'alimentant sur le krill à grandes profondeurs, et à des vitesses variant entre 2 et 5 m/s, maintiennent des taux de dépenses énergétiques entre 10 et 50 fois le taux métabolique basal des mammifères terrestres. À densités de proies égales, ces variations d'énergie utilisée peuvent réduire le rapport d'efficacité énergétique par des facteurs entre 2x et 3x, donc permettant aux petits et plus lents rorquals de chasser avec une efficacité comparable à celle des rorquals les plus grands et rapides. Notre analyse démontre aussi comment des vitesses d'approche plus lentes peuvent être reliées à la biomécanique de leur poche ventrale extensible, et à l'habilitée des proies à éviter d'être engouffrer. Ces minimums de vitesses sont importants car ils permettent une alimentation plus efficace énergétiquement.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".