A Tail of Four Fishes: An Analysis of Kinematics and Material Properties of Elongate Fishes
Bibliographic record
Abstract
The elongate body plan is present in many groups of fishes, and this morphology dictates functional consequences seen in swimming behavior. Previous work has shown that increasing the number of vertebrae, or decreasing the intervertebral joint length, in a fixed length artificial system increases stiffness. Tails with increased stiffness can generate more power from tail beats, resulting in an increased mean swimming speed. This demonstrates the impacts of morphology on both material properties and kinematics, establishing mechanisms for form contributing to function. Here, we wanted to investigate relationships between form and ecological function, such as differences in dietary strategies and habitat preferences among fish species. This study aims to characterize and compare the kinematics, material properties, and vertebral morphology of four species of elongate fishes: Anoplarchus insignis, Anoplarchus purpurescens, Xiphister atropurpureus, and Xiphister mucosus. We hypothesized that these properties would differ among the four species due to their differential ecological niches. To calculate kinematic variables, we filmed these fishes swimming volitionally. We also measured body stiffness by bending the abdominal and tail regions of sacrificed individuals in different stages of dissection (whole body, removed skin, and removed muscle). Finally, we counted the number of vertebrae from CT scans of each species to quantify vertebral morphology. Principal component and linear discriminant analyses suggested that the elongate fish species can be distinguished from one another by their material properties, morphology, and swimming kinematics. With this information combined, we can draw connections between the physical properties of the fishes and their ecological niches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".