No difference between critical and sprint swimming speeds for two galaxiid species
Bibliographic record
Abstract
Abstract Researchers have used laboratory experiments to examine how fish might be affected by anthropogenic alterations and conclude how best to adjust fish passage and culvert remediation designs in response. A common way to document swimming performance for this purpose is measuring fish critical swimming speed (Ucrit). Nonetheless, the Ucrit protocol as defined by Brett [(1964) Journal of the Fisheries Research Board of Canada, 21, 1183–1226] may be inappropriate for studying swimming performance and determining how it relates to upstream migration in benthic fish, as they may not actively swim throughout the entire Ucrit test. An alternative method to estimate swimming performance is sprint swimming speed (Usprint), which is suggested to be a measure of the burst speed of fish rather than maximum sustained swimming speed. The authors conducted comparative swimming performance experiments to evaluate whether Usprint can be used to compare swimming performance of benthic species to that of pelagic, actively swimming species. They measured individual swimming speeds of īnanga (Galaxias maculatus), an actively swimming pelagic species, and banded kōkopu (Galaxias fasciatus), a fish that exhibits benthic station‐holding behaviour, using both the Usprint and Ucrit test. Experiments revealed that no significant statistical difference between swimming speeds was estimated using the Ucrit and Usprint test protocols for both G. maculatus and G. fasciatus. The result of this study suggests that fish swimming speeds obtained using these two methods are comparable for the species used in this study. By using Usprint for benthic‐associated fish and Ucrit for pelagic fish, we may be able to compare a broader range of species' swimming abilities for use in a fish passage context.
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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 | 0.001 | 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".