Numerical investigation into the influence of scale shape on thehydrodynamics of fish scale arrays
Bibliographic record
Abstract
Aerodynamic drag is a problem that persists in many industries but has a particularly profound impact on energy consumption in the transportation sector. Naturally occurring surfaces have been optimized over thousands of years to handle the aerodynamic or hydrodynamic drag they experience. An understanding of these optimized features and underlying mechanisms would assist researchers and engineers to adapt these features in practical applications to reduce drag. One interesting feature that has received recent attention is the surface structure of fish scales. While these scales form an armor layer for fish, they also introduce a unique topography that interacts with the surrounding environment. Recent research has found that these fish scale arrays play an important role in delaying the transition from laminar to turbulent flow. However, studies have largely focused on studying the influence of a specific scale size and shape, yet research in the field of surface characterization has found that the scale size and shape can vary significantly between species and in different body sections of an individual fish. Given that the purpose of these scale variations is not well understood, there exists a need to study the influence of scale shape on the flow behaviour over these scale arrays. Knowledge of the role these variations play in modifying the flow structure can be used to enhance the design of structured surfaces which target drag reduction in practical engineering applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".