Bibliographic record
Abstract
Inspired by the reproductive success of plant species that employ bristled seeds for wind-borne dispersal, this study investigates the gust response of milkweed seeds, selected for their near-spherical shape. Gust-response experiments are performed to determine whether these porous bodies offer unique aerodynamic properties. Optical motion-tracking and particle image velocimetry (PIV) are used to characterize the dynamics of milkweed seed samples as they freely respond to a flow perturbation produced in an unsteady, gust wind tunnel. The observed seed acceleration ratio was found to agree with that of similar-sized soap bubbles as well as theoretical predictions, suggesting that aerodynamic performance does not degrade with porosity. Observations of high-velocity and high-vorticity fluid deflected around the body, obtained via time-resolved PIV measurements, suggest that there is minimal flow through the porous sphere. Therefore, despite the seed's porosity, the formation of a region of fluid shear, accompanied by vorticity roll-up around the body and in its wake, is not suppressed, as would normally be expected for porous bodies. Thus, the seeds achieve instantaneous drag exceeding that of a solid sphere (e.g. bubble) over the first eight convective times of the perturbation. Therefore, while the steady-state drag produced by porous bodies is typically lower than that of a solid counterpart, an enhanced drag response is generated during the initial flow acceleration period.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".