Robust flight performance of bumble bees with artificially induced wing wear
Bibliographic record
Abstract
We lack a mechanism that links wing wear with mortality in foraging social insects. This study tests the hypothesis that wing wear strongly degrades foraging flight performance, thereby providing a biomechanical explanation for the wing wear – mortality relationship. We examine the effect of simulated wing wear — wing area reduction and asymmetry — on the flight behaviour of bumble bee ( Bombus flavifrons Cresson, 1863) workers moving between vertically oriented flowers spaced 30 cm apart and arranged in a two-dimensional horizontal grid. Flight behaviour was measured in three dimensions as total flying distance, mean velocity, variability of velocity, maximum acceleration, maximum deceleration, percentage of time spent accelerating, and displacement from a straight line path between flowers. Loss of wing area had surprisingly little effect on flight behaviour. Viewed multivariately, bees with low asymmetry and low loss of mean area, or with high asymmetry and high loss of mean area, differed from the other three treatment groups. When bees were burdened with both high asymmetry and high loss of wing area, their between-flower flight path was less direct. Overall, flight behaviour of bumble bees was highly resilient to major changes in wing area and asymmetry in this simple foraging environment. The wing wear-associated causes of increased mortality remain elusive.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".