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Record W4284989362 · doi:10.1093/icb/icac117

High Wing-Loading Correlates with Dive Performance in Birds, Suggesting a Strategy to Reduce Buoyancy

2022· article· en· W4284989362 on OpenAlexaff
Anthony B. Lapsansky, Douglas R. Warrick, Bret W. Tobalske

Bibliographic record

VenueIntegrative and Comparative Biology · 2022
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsUniversity of British Columbia
FundersDivision of Emerging Frontiers in Research and InnovationCornell Lab of OrnithologyNational Science Foundation
KeywordsWingBuoyancyBiologyEnvironmental scienceMechanicsAerospace engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Diving birds are regarded as a classic example of morphological convergence. Divers tend to have small wings extending from rotund bodies, requiring many volant species to fly with rapid wingbeats, and rendering others flightless. The high wing-loading of diving birds is frequently associated with the challenge of using forelimbs adapted for flight for locomotion in a "draggier" fluid, but this does not explain why species that rely exclusively on their feet to dive should have relatively small wings, as well. Therefore, others have hypothesized that ecological factors shared by wing-propelled and foot-propelled diving birds drive the evolution of high wing-loading. Following a reexamination of the aquatic habits of birds, we tested between hypotheses seeking to explain high wing-loading in divers using new comparative data and phylogenetically informed analyses. We found little evidence that wing-propelled diving selects for small wings, as wing-propelled and foot-propelled species share similar wing-loadings. Instead, our results suggest that selection to reduce buoyancy has driven high wing-loading in divers, offering insights for the development of bird-like aquatic robots.

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 score0.138
Threshold uncertainty score0.717

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 head0.032
GPT teacher head0.259
Teacher spread0.227 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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