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Record W2167919949 · doi:10.1242/jeb.054189

BLUE WHALE-SIZED MOUTHFULS MAKE FORAGING SUPER EFFICIENT

2010· article· en· W2167919949 on OpenAlexaboutno aff
Kathryn Knight

Bibliographic record

VenueJournal of Experimental Biology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsWhaleKrillForagingFisheryOceanographyBiologyEcologyGeology

Abstract

fetched live from OpenAlex

When a blue whale dives to the krill fields where it feeds, it can remain submerged for anything up to 15 min. However, Bob Shadwick from the University of British Columbia, Canada, explains that blue whales may be able to dive for longer, because of the colossal oxygen supplies they could carry in their blood and muscles, so why don't they? ‘The theory was that what they are doing under water must use a lot of energy,’ says Shadwick. Explaining that the whales feed by lunging repeatedly through deep shoals of krill, engulfing their own body weight in water before filtering out the nutritious crustaceans, Shadwick says, ‘It was thought that the huge drag effect when they feed and reaccelerate this gigantic body must be the cost’. However, measuring the energetics of blue whale lunges at depth seemed almost impossible until Shadwick and his student Jeremy Goldbogen got chatting to John Hildebrand, John Calambokidis, Erin Oleson and Greg Schorr (p. 131). Hoping to record feeding blue whale conversations, Calambokidis and his colleagues had skilfully attached hydrophones, pressure sensors and two-axis accelerometers to the elusive animals and tracked them to recover the valuable data. Shadwick and Goldbogen realised that they could use Calambokidis's measurements to calculate the energetic cost of blue whale lunges.Analysing the behaviour of each whale, Goldbogen saw that dives lasted between 3.1 and 15.2 min and a whale could lunge as many as 6 times during a single dive. Having found previously that he could correlate the acoustic noise of the water swishing past the hydrophone with the speed at which a whale was moving, Goldbogen calculated the blue whales' speeds as they lunged repeatedly during each dive. Next the team had to calculate the forces exerted on the whales as they accelerated their colossal mouthful of water. Noticing that the whales' mouths inflated almost like a parachute as they engulfed the krill, Goldbogen tracked down parachute aerodynamics expert Jean Potvin to help them build a mathematical model to calculate the forces acting on the whales as they lunged. With Potvin on the team, they were able to calculate that the whales used between 3226–8071 kJ of energy during each lunge. But how did this compare with the amount of energy that the whales could extract from each gigantic mouthful of krill?Goldbogen estimated the volume of the whales' mouths by searching the whaling literature for morphological data and teamed up with paleontologist Nick Pyenson to measure the size of blue whale jaw bones in several natural history museums. He also obtained krill density values from the literature – which are probably on the low side. Then he calculated the volume of water and amount of krill that a whale could engulf and found that the whales could consume anything from 34,776 kJ up to an unprecedented 1,912,680 kJ from a single mouthful of krill, providing as much as 240 times as much energy as the animals used in a single lunge. And when the team calculated the amount of energy that a whale could take on board during a dive, they found that each foraging dive could provide 90 times as much energy as they used.Shadwick admits that he was initially surprised that the whales' foraging dives were so efficient. ‘We went over the numbers a lot,’ he remembers, but then he and Goldbogen realised that the whales' immense efficiency makes sense. ‘The key to this is the size factor because they can engulf such a large volume with so much food in it that it really pays off,’ says Shadwick.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.003

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 head0.016
GPT teacher head0.277
Teacher spread0.262 · 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".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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