Does swimming activity influence gas bubble trauma in fish?
Bibliographic record
Abstract
Abstract Total dissolved gas (TDG) supersaturation from sources such as hydroelectric dams can cause harmful bubble growth in the tissues of aquatic animals, known as gas bubble trauma (GBT). Locomotion is known to exacerbate bubble growth in tissues during decompression under certain conditions (such as in diving animals), possibly because of increased bubble nucleation. As with decompression sickness, GBT is caused by the supersaturation of tissues with gas, and thus we hypothesize that locomotion promotes bubble nucleation in the tissues of fish exposed to TDG supersaturation. Many previous laboratory studies have tested the effects of TDG on fish exposed to low‐velocity, non‐directional flow, whereas fish in field conditions are exposed to higher‐velocity flows and are likely more active. Therefore, it is important to understand the effects of locomotion on GBT to apply laboratory results to active fish in field conditions. We exposed rainbow trout (Oncorhynchus mykiss) to either control (100% TDG) or TDG supersaturation (123% TDG) in either static or flowing water conditions (1.8 Bl/s) and recorded time to 50% loss of equilibrium (LOE). We observed no statistically significant difference in time to 50% LOE between flow conditions. Given the lack of statistically significant difference between static and flowing water, our findings indicate that results from GBT experiments on rainbow trout in non‐directional flow are applicable to more active individuals.
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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) | 0.002 | 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".