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Enregistrement W2900460812 · doi:10.1242/jeb.193664

Ants swing and probe with antennae to stay on scent track

2018· article· en· W2900460812 sur OpenAlexaboutno aff
Kathryn Knight

Notice bibliographique

RevueJournal of Experimental Biology · 2018
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueInsect and Arachnid Ecology and Behavior
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCreaturesVisual artsComputer scienceCommunicationHistoryArtPsychologyArchaeologyNatural (archaeology)

Résumé

récupéré en direct d'OpenAlex

Creatures that negotiate the world in the dark tend to have a great sense of smell. Some sniff out friends, food and home with sensitive noses, while insects and crustaceans follow odour trails with pairs of waving antennae. Ryan Draft from Harvard University, USA, explains that ants are capable of interpreting the subtle differences in perceived odour strength picked up by their antennae while scurrying along scent trails. However, he adds, ‘little attention has been given to the actual behavioural strategies and the patterns of antennae movements’. Intrigued by the mechanisms that allow animals to navigate by their noses, Draft and colleagues Matthew McGill, Vikrant Kapoor and Venkatesh Murthy, also from Harvard, decided to get to the bottom of exactly how black carpenter ants (Camponotus pennsylvanicus) manoeuvre their antennae as they track an odour trail.Kapoor designed an enclosed infra-red illuminated arena where McGill and Draft could lay scent trails and film the ants’ responses in the dark. ‘We didn't know what the animals would respond to and what they could and couldn't do,’ says Draft, recalling how he and McGill screened a wide range of continuous tracks. ‘We tried … straight, curved, zig-zagged and branching trails. We also explored dashed and gapped line trails and even random dots and random scratches’, says Draft. Even then, some ants were keen to explore, while others refused to cooperate. McGill and Draft also filmed how ants that had lost an antenna coped, before patiently tracking the positions of the tips of each ant's antennae, and their head and body to accurately reconstruct their manoeuvres.Comparing the intact ants’ movements before and after they locked onto the odour trail, the team could see that the insects that were searching for a trail held their antennae apart and moved the tips over a small range. However, when the ants encountered an odour trail, they swung the antennae tips over wider arcs and performed in one of three possible ways. On some occasions the ants locked their antennae onto the trail, while weaving their bodies back and forth across the path (the authors call this swinging motion sinusoidal behaviour). In the second strategy, the ants stationed themselves in a static position close to the trail while whisking the antennae back and forth across it to learn more about the odour distribution (probing behaviour). But once the ant was certain that it had locked onto a trail, it hugged the path tightly, whisking the antennae back and forth to the edges of the odour band, always holding the trail between the two antennae (trail following).Draft comments, ‘we saw … different uses for the left and right antenna while tracking’, and adds that this bias was boosted when the ants negotiated a curved trail, holding the antenna that was on the inside of the curve in the odour trail as they followed it around. In addition, the team noticed that the ants moved their antennae in the opposite direction to their bodies, to ensure that they were always located in different regions of the trail to enhance any odour differences between the two locations. And, when the ants were deprived of one antenna they coped remarkably well – compensating by sweeping the remaining antenna through a wider angle – although their precision decreased.‘The big take away for us is just how sophisticated ants are in using their antennae to gather signals from the environment’, says Draft, who adds, ‘This is the first step to understand how sensory signals guide behaviour’.

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)0,002
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 enseignants0,006
Score d'incertitude au seuil0,018

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,002
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,005

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 Opus0,015
Tête enseignante GPT0,300
Écart entre enseignants0,285 · 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

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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