Comparisons of swimming performance in rainbow trout using constant acceleration and critical swimming speed tests
Bibliographic record
Abstract
Maximum swimming performance of seasonally acclimated rainbow trout Oncorhynchus mykiss was compared among short‐duration constant acceleration tests (Umax) and with the well established, but longer duration critical swimming speed (Ucrit) test. The present results show that Umax was insensitive to a range of acceleration rates that differed by more than three‐fold. Thus, test duration could be reduced from 58 to 18 min without affecting the estimate of Umax. The value of Umax, however, was up to 57% higher than Ucrit. Only the slowest acceleration rate tested (an increase of 1 cm s−1 every min) had a significantly lower Umax, and this was up to 19% higher than Ucrit. Even so, the potential saving in the test duration was small (70 v. 90 min) when compared with a ramp‐Ucrit test (a standard Ucrit test but with the water velocity initially ramped to c. 50% of the estimated Ucrit). Therefore, swim tests that are appreciably shorter in duration than a ramp‐Ucrit test result in Umax being appreciably greater than Ucrit. An additional discovery was that the ramp‐Ucrit performance of cold‐acclimated rainbow trout was independent of the recovery period between tests. These results may prove useful in making comparisons among different swim test protocols and in designing swim tests that assess fish health and toxicological impacts.
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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) | 0.000 | 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 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".