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Record W2204070191 · doi:10.1139/cjz-2014-0317

Shape of wing wear fails to affect load lifting in common eastern bumble bees (<i>Bombus impatiens</i>) with experimental wing wear

2015· article· en· W2204070191 on OpenAlexaffvenue
Jordan C. Roberts, Ralph V. Cartar

Bibliographic record

VenueCanadian Journal of Zoology · 2015
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWingWing loadingWing configurationBiologyAerodynamicsLift (data mining)Structural engineeringAngle of attackAerospace engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

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.

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 score0.277
Threshold uncertainty score0.999

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 head0.019
GPT teacher head0.227
Teacher spread0.208 · 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

Citations15
Published2015
Admission routes2
Has abstractyes

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