Exploring the mechanics of thunniform propulsion: a model study
Bibliographic record
Abstract
Thunniform propulsion is considered a case study in convergent evolution. Independently derived at least four times, it is characterized by uniquely high lift-based thrust and efficient performance. As such, it has been the focus of studies from biologists, engineers, and physicists. Unfortunately, direct physical measurements of this phenomenon are difficult to obtain. Therefore, the majority of research so far has consisted of theoretical modeling or experimental testing with models of low biofidelity. We created a test apparatus that would more accurately mimic thunniform propulsion as seen in the skipjack tuna (Katsuwonus pelamis (L., 1758)). Motion parameters and swimming speeds, as well as caudal fin size, shape, and material properties, were all taken into account and closely matched with in vivo measurements. Instantaneous lateral and in-flow forces were measured in tests over a range of motion regimes. Overall, general motion parameter requirements for thrust generation were determined and quantified. Thrust production, of up to 0.42 N (per whole caudal fin) with a coefficient of thrust of approximately 0.2, were in line with estimates of whole-body drag. Propulsive efficiency estimates were low (≤35%) compared with estimates in the literature of up to 90%. Quasi-static analysis was also conducted and shown to underpredict measured thrust values by up to 50%.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".