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Record W2111216153 · doi:10.2108/zsj.20.311

Foraging Navigation of Hornets Studied in Natural Habitats and Laboratory Experiments

2003· article· en· W2111216153 on OpenAlexfundno aff
Yoshihiro Toh, Jun-ya Okamura

Bibliographic record

VenueZOOLOGICAL SCIENCE · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsnot available
FundersDalhousie University
KeywordsForagingSensory cueBiologyLandmarkOlfactory cuesCommunicationEcologyComputer visionComputer scienceOlfactionPsychologyNeuroscience

Abstract

fetched live from OpenAlex

Foraging flights have been studied in three species of hornets (Vespa mandarinia, V. simillima and V. analis) in the field and the laboratory. Hornets seem to use multiple navigational cues for visiting a familiar feeding place. They could orient towards the feeding place immediately after they rose in air from the nest without directly viewing the feeder. They could visit the feeding place after dark at a luminosity 8 lux. These data suggest that they can navigate for some distance with few external cues. Hornets also seem to rely on visual cues for their mid-range navigation. They used some structures on their way as navigational landmarks to negotiate. Individual hornets are supposed to have their own landmarks. Olfactory cues seem to be used to find a new feeding place or to recruit other member. In the approach flight hornets seemed to use multiple visual cues such as the visual characteristics of the feeder and the wider scenery around the feeder. Even if the feeder in training was removed during the test, they flew with a smooth course as if they were pin-pointing the missing feeder, but without sitting on the ground. Hornets learnt how to fly to reach the feeder without external cues after passing by the last visual landmark under conditions with extremely poor visual cues. The present work suggests that hornets retain multiple navigational cues during repeated foraging behavior, and which cues they use seems to depend upon environmental conditions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

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

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 head0.291
Teacher spread0.277 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations17
Published2003
Admission routes1
Has abstractyes

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Same venueZOOLOGICAL SCIENCESame topicInsect and Arachnid Ecology and BehaviorFrench-language works237,207