Effect of water temperature on laboratory swimming performance and natural activity levels of adult largemouth bass
Bibliographic record
Abstract
Although locomotory performance in vertebrates is related to fitness, most performance tests are conducted in a laboratory setting, or in a manner that forces the organism to move not of their own volition. Biotelemetry offers the possibility to measure voluntary activity in a natural setting and provides the opportunity to combine laboratory-derived data with field studies on wild fish. In this study, it was found that laboratory- and field-based measurements of swimming performance and voluntary activity resulted in similar general seasonal trends, though each measurement assessed a different swimming type. In the field, all swimming metrics were lower at cooler water temperatures and were lowest during early winter (mean daily activity = 0.016 BL/s; mean voluntary swimming activity = 0.04319 BL/s; maximum swimming speed = 0.17 BL/s). In the laboratory, fish acclimatized to 25.0, 14.0, and 7.5 °C decreased swimming performance (Ucrit) with water temperature (25.0 °C (2.17 BL/s); 14.0 °C (1.69 BL/s); 7.5 °C (1.17 BL/s). Although some species and tissues have been shown to exhibit different degrees of thermal adaptation, these results show that swimming, one of the most important functions in fish, is largely dependent on environmental temperature, at least in largemouth bass ( Micropterus salmoides (Lacepède, 1802)).
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 | 0.000 | 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 | 0.001 | 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".