The novel characteristics of pterosaurs: biological inspiration for robotic vehicles
Bibliographic record
Abstract
Bioinspiration and biomimetic have led to a variety of robotic designs, especially small autonomous unmanned aerial vehicles for urban environment that have taken cues from birds, bats, and insects.The ease with which these fl ying animals negotiate confi ned obstacle-cluttered airspace has long inspired engineers for designing small robots for surveillance and reconnaissance missions.However, one group of extinct fl ying animals, which fl ew over the heads of dinosaurs and dominated the Mesozoic skies for 160 million years, have been largely overlooked for designing small aircrafts, partly because they are extinct and their fossils are diffi cult to study because of preservational deformation.Recently, exquisite pterosaur fossils have been discovered, which provide critical insights into their dynamics.Tapejara wellnhoferi, a pterodactyloid from the Early Cretaceous (~110 million years ago) of Brazil, provides a platform that is particularly valuable for biomimicry of a robotic vehicle.This pterodactyloid had sophisticated sensor mechanisms for determining its aerodynamics, had a cranial crest that was destabilizing but provided agility, had highly articulated wings that enabled precise shape control, and had the ability to fl y, walk, and sail.An initial design for a robotic vehicle is described, which incorporates some of the characteristics of the Tapejara wellnhoferi.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".