Bibliographic record
Abstract
ABSTRACT Swimming speeds and endurance vary with species and body morphology, fish length, water temperature and other variables. Data on fish swimming performance are collected from various swim tests conducted in the laboratory or the field. Tests include stamina tunnels where fish are constrained, long pipes with pressure flow, open channels where fish are either constrained or free to volitionally swim and field studies with culverts or other hydraulic structures where fish passage is assessed. Studies on swimming performance and data availability on various species, mostly from stamina tunnels, have been increasing since the 1960s, with more emphasis on volitional channels since the 1990s. An extensive database on fish swimming performance was generated from the literature. Fish tested represent a broad range of species and individuals within many species. There are insufficient data for many species, and significant regressions for speed versus endurance are not available for these species. Fatigue curves of groups of species using an ecohydraulic approach with dimensionless quantities improved regressions when compared with body lengths per second versus time. Furthermore, it was found that the square root of body length is a better scale for fish speed. Although variability in swimming performance exists between species and individuals within a species, data analyses indicate broad similarities in relative performance for groups of species of similar morphology or swimming mode. The ecohydraulic approach allowed the use of limited data sets in analyses for groups of species. Significant speed–time regressions were developed for three groups, the Eel group, the Trout group and the Sturgeon group. Estimates of fish fatigue time with different confidence levels may be useful when considering physiological aspects in practical applications. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| 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) | 0.003 | 0.001 |
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".