High Wing-Loading Correlates with Dive Performance in Birds, Suggesting a Strategy to Reduce Buoyancy
Bibliographic record
Abstract
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 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) | 0.002 | 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".