MétaCan
Menu
Back to cohort
Record W2096031029 · doi:10.1242/jeb.051730

What causes wing wear in foraging bumble bees?

2011· article· en· W2096031029 on OpenAlexafffund
Danusha J. Foster, Ralph V. Cartar

Bibliographic record

VenueJournal of Experimental Biology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsWingForagingWing loadingBiologyEcologyZoologyAerospace engineeringEngineeringAngle of attackAerodynamics

Abstract

fetched live from OpenAlex

Flying is an ecologically important behaviour in many insects, but it often results in permanent wing damage. Although wing wear in insects is often used as a method to determine insect age, and is associated with an increased risk of mortality, the causes of wing wear are unresolved. In this paper, we examine whether wing use while foraging explains wing wear in bumble bees (Bombus spp.). Wing wear may result from three distinct flight characteristics during foraging: time spent in flight, flight frequency and frequency of wing collisions with vegetation. To test these hypotheses for causes of wing wear, we recorded digital video of individually marked bumble bees foraging in nature on 12 different plant species that result in variation in these flight characteristics, and recaptured these individuals to photograph their wings over time. Bumble bees with a higher frequency of wing collisions showed an increased loss of wing area, which became more severe over time. Neither time in flight nor flight frequency was uniquely and significantly associated with wing wear. Therefore, the collision frequency hypothesis best explained wing wear in bumble bees. We conclude that wing use during foraging in bumble bees results in wing wear. Wing wear reflects behaviour, not simply age. Because wing wear has elsewhere been shown to increase mortality, this study provides an important mechanism linking foraging behaviour with lifespan.

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 score0.143
Threshold uncertainty score0.215

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 head0.097
GPT teacher head0.276
Teacher spread0.179 · 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

Citations108
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Experimental BiologySame topicPlant and animal studiesFrench-language works237,207