Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".