Aerodynamic implications of gull's drooped wing-tips
Bibliographic record
Abstract
When in gliding flight, gulls are observed to adopt a drooped wing-tip configuration. This paper investigates whether this configuration might represent an aerodynamic optimum or if it is the result of constraints imposed by the gull's anatomy. A computational model was developed for the aerodynamic performance of a gull in gliding flight. This model was used in conjunction with both global and local optimizers to determine the most aerodynamically optimal configuration for cases where the gull was constrained to move its wing within its natural flapping cycle as well as when the wing had full freedom of motion. The results of this analysis determined the best wing configuration for a gull in gliding flight and demonstrated that such a configuration not only had the highest lift-to-drag ratio but also could be achieved within the constraints of the kinematics of the gull wing. These results are of interest outside studies of gulls, since the drooped wing-tip configuration could be relevant for new designs of small air vehicles.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".