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Record W2900460812 · doi:10.1242/jeb.193664

Ants swing and probe with antennae to stay on scent track

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

Bibliographic record

VenueJournal of Experimental Biology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsCreaturesVisual artsComputer scienceCommunicationHistoryArtPsychologyArchaeologyNatural (archaeology)

Abstract

fetched live from 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’.

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)0.002
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 score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.005

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 head0.015
GPT teacher head0.300
Teacher spread0.285 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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