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Record W2134790972 · doi:10.1242/jeb.063842

THE COST OF WHALE GULPS: SIZE MATTERS!

2012· article· en· W2134790972 on OpenAlexaffabout
Jessica U. Meir

Bibliographic record

VenueJournal of Experimental Biology · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsForagingBaleenWhaleEnergeticsHumpback whalePropulsive efficiencyCetaceaPredationCreaturesFisheryDragBiologyEcologyPropulsionPaleontologyEngineering

Abstract

fetched live from OpenAlex

From basic physiology texts to studies of consummate diving animals, the notion that diving capacity increases with body size follows intuitively from the fact that bigger bodies denote larger oxygen stores, but lower metabolic rates. Recent research by Jeremy Goldbogen at Cascadia Research Collective and collaborators in the US and Canada, however, demonstrates that in the most massive breath-hold divers, size matters in a different manner. Large rorquals (blue, fin and humpback whales) forage by lunge feeding, an extraordinary process by which these mammoths of the sea accelerate to high speed, engulf immense volumes of prey-laden water, and purge and filter their big gulps. Such exceptional feeding manoeuvres are not without consequence, however, as this technique requires high drag and incurs great energetic costs for these colossal creatures. Not surprisingly, these whales don't dive for as long as other large divers, including other whales that don't partake in lunge feeding. After generating a bounty of data on the foraging kinematics and energetics of baleen whales, Goldbogen and colleagues were inspired to take a closer look at the scaling of lunge-feeding performance in these titanic filter feeders. Bigger mouths mean bigger gulps for bigger whales, but as the energetic requirements of feeding are also predicted to increase with size, they hypothesized that the cost of a lunge might be disproportionately higher in large rorquals, thereby limiting their dive capacity.The researchers compared diving and lunge-feeding performance among three species of rorquals foraging on krill, ranging in size from the (relatively!) modest humpback whale, to the mid-sized fin whale, and the heftiest animal on Earth, the blue whale. They gathered morphological data (body mass, length, etc.) and set out to derive parameters such as mouth and frontal body area, using geometric models and measurements like skull width and jaw length to estimate gulp size. Employing data from previously deployed acoustic tags and time–depth recorders, Goldbogen and colleagues obtained swimming speed, dive duration and depth, and dive and lunge profiles. Finally, they applied hydro-mechanical and energetic models to estimate the drag required for gulps and the energy exhausted during lunges.The team revealed that not only is drag relatively higher in bigger whales, as a result of a larger mouth area, but also lunge speed increases with body size, further increasing drag in larger whales. Although maximum dive duration and depth were not different between the three species, the largest whales took longer to filter their mouthfuls. This means that humpbacks can lunge more per dive (and per minute of dive) than can fin or blue whales. Higher, sustained drag in larger rorquals requires more energy. When accounting for mass differences, the cost per gulp for blue and fin whales was three- and two-times higher, respectively, than for humpbacks. Despite a host of advantages stemming from the ability to make long dives (access to deeper waters, more time to search for and exploit prey, etc.), these behemoths forfeit enhanced diving capacity in favour of optimized gulps. This trade-off sets large rorquals apart from other true divers, probably allowing them to cash in on patchily distributed prey aggregations by taking gargantuan gulps.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.002

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.

Opus teacher head0.021
GPT teacher head0.320
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2012
Admission routes2
Has abstractyes

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