Bibliographic record
Abstract
Swimming performance tests of fish have been integral to studies of muscle energetics, swimming mechanics, gas exchange, cardiac physiology, disease, pollution, hypoxia and temperature. This paper describes a flexible protocol to assess fish swimming performance using equipment in which water velocity can be controlled. The protocol involves one to several stepped increases in flow speed that are intended to cause fish to fatigue. Step speeds and their duration can be set to capture swimming abilities of different physiological and ecological relevance. Most frequently step size is set to determine critical swimming velocity (U(crit;)), which is intended to capture maximum sustained swimming ability. Traditionally this test has consisted of approximately ten steps each of 20 min duration. However, steps of shorter duration (e.g. 1 min) are increasingly being utilized to capture acceleration ability or burst swimming performance. Regardless of step size, swimming tests can be repeated over time to gauge individual variation and recovery ability. Endpoints related to swimming such as measures of metabolic rate, fin use, ventilation rate, and of behavior, such as the distance between schooling fish, are often included before, during and after swimming tests. Given the diversity of fish species, the number of unexplored research questions, and the importance of many species to global ecology and economic health, studies of fish swimming performance will remain popular and invaluable for the foreseeable future.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".