Swimming performance in juvenile shortnose sturgeon (Acipenser brevirostrum): the influence of time interval and velocity increments on critical swimming tests
Bibliographic record
Abstract
The most utilized method to measure swimming performance of fishes has been the critical swimming speed (UCrit) test. In this test, the fish is forced to swim against an incrementally increasing flow of water until fatigue. Before the water velocity is increased, the fish swims at the water velocity for a specific, pre-arranged time interval. The magnitude of the velocity increments and the time interval for each swimming period can vary across studies making the comparison between and within species difficult. This issue has been acknowledged in the literature, however, little empirical evidence exists that tests the importance of velocity and time increments on swimming performance in fish. A practical application for fish performance is through the design of fishways that enable fish to bypass anthropogenic structures (e.g. dams) that block migration routes, which is one of the causes of world-wide decline in sturgeon populations. While fishways will improve sturgeon conservation, they need to be specifically designed to accommodate the swimming capabilities specific for sturgeons, and it is possible that current swimming methodologies have under-estimated the swimming performance of sturgeons. The present study assessed the UCrit of shortnose sturgeon using modified UCrit to determine the importance of velocity increment (5 and 10 cm s−1) and time (5, 15 and 30 min) intervals on swimming performance. UCrit was found to be influenced by both time interval and water velocity. UCrit was generally lower in sturgeon when they were swum using 5cm s−1 compared with 10 cm s−1 increments. Velocity increment influences the UCrit more than time interval. Overall, researchers must consider the impacts of using particular swimming criteria when designing their experiments.
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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 | 0.000 |
| 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".