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Enregistrement W2980469570 · doi:10.1242/jeb.215822

Baby bats get to grips with echolocation before taking to the wing

2019· article· en· W2980469570 sur OpenAlexaboutno aff
Kathryn Knight

Notice bibliographique

RevueJournal of Experimental Biology · 2019
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueBat Biology and Ecology Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHuman echolocationCreaturesCommunicationHistoryPsychologyArchaeologyNeuroscience

Résumé

récupéré en direct d'OpenAlex

It's generally wise to learn how to walk before trying to run. YouTube is awash with the first tottering steps of creatures ranging from pandas and giraffes to polar bears and elephants. But bat pups face a different challenge. It would seem to make sense for baby bats to get to grips with the finer details of echolocation before taking their first tentative flaps and, most essentially, that they hone the specialised series of cries that guide them in for landing. ‘There has been lots of previous work looking at how flight and echolocation develop individually in juvenile bats, but understanding how both traits develop with respect to one another hasn't really been examined’, says Heather Mayberry from the University of Toronto, Mississauga, Canada. Having explored how pups transition from producing the calls that they use to stay in touch with mum to adult-like echolocation calls during her Master's degree with Paul Faure at McMaster University, Canada, Mayberry decided to integrate her interest in bat development with how they coordinate echolocation and flight when she joined John Ratcliffe in Mississauga. This time, she focused on finding out how fledgling bats develop their acoustic guidance system before taking to the wing.‘Working with baby bats is rewarding’, says Mayberry, who recalls gently separating newborn pups from their mothers to record their earliest attempts at echolocation as they made their first attempts at flight. ‘I held the baby bats and let them move themselves off my hand over a foam landing pad’, says Mayberry. Over the course of the next month, Mayberry recorded their developing calls as the pups became more independent, until they were fully grown at the age of 32 days. Initially, the tiny youngsters simply flopped off Mayberry's hand onto the soft sponge beneath, making no attempt to flap their wings. However, around the age of 5–6 days, the pups began attempting to flap, albeit unsuccessfully, until they reached 16–17 days, when the youngsters’ efforts became more successful, and they managed to propel themselves forward as they fell, before taking full control of their wings around the age of 24 days. But how did their voices develop over that time?Analysing the high-pitched cries that the youngsters made as they fell and searching for clusters of calls known as sonar strobe groups – which indicate that the bat is alert – Mayberry was intrigued when she realised that some of the tumbling pups were able to string calls together around the age of 6 days. And when she analysed the pups’ cries for evidence of the distinctive buzz that guides the adults in to land, the youngsters were producing them at around 17 days. ‘We were surprised that pups were able to produce adult-like call groupings and landing buzzes so early in their development’, says Mayberry. However, the youngsters weren't timing their landing buzzes well, with several producing the distinctive cries even before Mayberry had released them from her hand; they still needed to learn how to coordinate the cries with flight to effect an effortless touchdown. And when Mayberry compared the pups’ physical development with the age at which they mastered flight, it was apparent that the youngsters whose wings had grown faster than their bodies – to reach the body weight to wing area ratio of adults – took to their wings first.So, bat pups start getting to grips with echolocation long before they have need of their acoustic guidance systems, and Mayberry now hopes to find out how the pups learn to coordinate their breathing with their echolocation calls as they develop into adults.

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)0,001
score de la tête « metaresearch » (Gemma)0,003
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 enseignants0,016
Score d'incertitude au seuil0,054

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,003
Communication savante0,0020,002
Science ouverte0,0010,002
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0160,017

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 Opus0,022
Tête enseignante GPT0,269
Écart entre enseignants0,247 · 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
Publié2019
Routes d'admission1
Résumé présentoui

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Même revueJournal of Experimental Biology→Même sujetBat Biology and Ecology Studies→Travaux en français237 207→