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Enregistrement W2167919949 · doi:10.1242/jeb.054189

BLUE WHALE-SIZED MOUTHFULS MAKE FORAGING SUPER EFFICIENT

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

Notice bibliographique

RevueJournal of Experimental Biology · 2010
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueMarine animal studies overview
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWhaleKrillForagingFisheryOceanographyBiologyEcologyGeology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,033

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0100,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,016
Tête enseignante GPT0,277
Écart entre enseignants0,262 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2010
Routes d'admission1
Résumé présentoui

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