Experimental study of the hydrodynamic interaction between a fish and an actively pitching airfoil
Bibliographic record
Abstract
The phenomenon of fish schooling - coordinated swimming of fish in polarized groups of specific spatial formations - is commonly observed in several species of fish. Fish schooling may even provide hydrodynamic advantages reducing the overall swimming cost of the group. To date, the role of hydrodynamics in coordinated swimming is not completely understood as it is difficult to separately study the role of hydrodynamic interaction from other forms of interaction between the fish. Here, we propose a statistical methodology based on information theoretic tools and flow velocity measurements, that can potentially tease out the hydrodynamic interaction pathways from visual and tactile ones. To avoid experimental confounds from bidirectional interactions and objectively understand cause-and-effect relationships, we design a robotic platform that mimics the behavior of two fish swimming in-line in a controlled setup inside a water channel. We examine the response of a flag to the fish-like unsteady wake generated by an actively pitching airfoil located upstream. We systematically quantify the passive hydrodynamic effect by studying the flapping motion of the flag located downstream of the airfoil in response to both periodic pitching and less predictable, random startling motion of the upstream airfoil. The study integrates experimental biomimetics with information theory to establish a deeper understanding of hydrodynamics in fish schooling.
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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 | 0.001 |
| 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 | 0.000 | 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".