Video images and undulatory movement equation of pangasius sanitwongsei’s caudal fin of steady swimming fish
Bibliographic record
Abstract
Experimental hydrodynamics imaging of four Pangasius sanitwongsei were considered.A quantitative characterization of caudal fi n is presented in this article.Steady swimming of four P. sanitwongsei with different total length was studied experimentally and taped by high-speed digital video, and undulatory movement of each fi sh at different velocity was revealed.The pattern of body undulatory movement of the fi sh was drawn from the video images.Three main factors that determine the fi sh swimming behavior are Reynolds number, Strouhal number and shape.In this study, Lf/L was chosen as a characteristic of shape, where Lf was the distance from the start of the head to the end of the head.This is a major point and displays less variation of head to the more variation of the body and caudal fi n.L is the length of the fi sh body.The relationship between Reynolds number and Strouhal number of four P. sanitwongsei with different Lf/L were studied here.Then, the relationship between effective non-dimensional parameters in thrust force and kinematic parameters was found.As a result, an experimental equation was formulated.This equation indicates that, as much as the ratio of the end part of fi sh with high undulatory movement (body and caudal fi n) to the total length goes up, the ratio of amplitude to the total length increases.Consequently, there was an increase in displacement and thrust force also.Then, undulatory movement equation of fi sh swimming was calculated by fi tting a second-order function that describes wave amplitude of this type of fi sh.All the fi nding in these researches could be applied to design a robotic fi sh.
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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) | 0.002 | 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".