Unsteady flow affects swimming energetics in a labriform fish (<i>Cymatogaster aggregata</i>)
Bibliographic record
Abstract
Unsteady water flows are common in nature, yet the swimming performance of fishes is typically evaluated at constant, steady speeds in the laboratory. We examined how cyclic changes in water flow velocity affect the swimming performance and energetics of a labriform swimmer, the shiner surfperch, Cymatogaster aggregata, during station holding. Using intermittent-flow respirometry, we measured critical swimming speed (Ucrit), oxygen consumption rates (O2) and pectoral fin use in steady flow versus unsteady flows with either low- [0.5 body lengths (BL) s(-1)] or high-amplitude (1.0 BL s(-1)) velocity fluctuations, with a 5 s period. Individuals in low-amplitude unsteady flow performed as well as fish in steady flow. However, swimming costs in high-amplitude unsteady flow were on average 25.3% higher than in steady flow and 14.2% higher than estimated values obtained from simulations based on the non-linear relationship between swimming speed and oxygen consumption rate in steady flow. Time-averaged pectoral fin use (fin-beat frequency measured over 300 s) was similar among treatments. However, measures of instantaneous fin use (fin-beat period) and body movement in high-amplitude unsteady flow indicate that individuals with greater variation in the duration of their fin beats were better at holding station and consumed less oxygen than fish with low variation in fin-beat period. These results suggest that the costs of swimming in unsteady flows are context dependent in labriform swimmers, and may be influenced by individual differences in the ability of fishes to adjust their fin beats to the flow environment.
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 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) | 0.001 | 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".