Shape of wing wear fails to affect load lifting in common eastern bumble bees (<i>Bombus impatiens</i>) with experimental wing wear
Bibliographic record
Abstract
Wing wear reflects the accumulation of irreversible damage to an insect’s wings over its lifetime and this damage should influence flight performance. In the case of bumble bees, flight seems robust to variation in wing-area asymmetry and air pressure, but not to loss of wing area. However, how the pattern of wing wear affects flight performance remains unstudied. In nature, wing wear typically occurs in a ragged and haphazard pattern along the wing’s trailing margin, a shape strikingly different from the straight cut applied in past studies. In this study, we test if shape of wing wear (implemented as four distinct treatments plus a control) affects maximum load-lifting capabilities and wingbeat frequency of worker common eastern bumble bees (Bombus impatiens Cresson, 1863). We found that shape of wing wear of 171 mg bees had no detectable effect on maximum load-lifting capability (detectable effect size = 18 mg) or on wingbeat frequency (detectable effect size = 15 Hz), but that loss of wing area reduced load-lifting capability and increased wingbeat frequency. The importance of wing area in explaining the load-lifting ability of bumble bees is reinforced in this study. But, paradoxically, shape of wing wear did not detectably affect lift generation, which is determined by unsteady aerodynamic forces in these lift-reliant insects.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".