The effect of total dissolved gas supersaturation on gas bubble trauma in juvenile rainbow trout (<i>Oncorhynchus mykiss</i>), juvenile kokanee (<i>Oncorhynchus nerka</i>), and two age classes of white sturgeon (<i>Acipenser transmontanus</i>)
Bibliographic record
Abstract
Hydroelectric dams are an important source of electricity globally, but they can also cause total dissolved gas (TDG) supersaturation in rivers. TDG supersaturation can harm fish through a condition called gas bubble trauma (GBT), which has been studied primarily in salmonids, such as rainbow trout and steelhead salmon (Oncorhynchus mykiss), but seldomly in non-salmonids like white sturgeon (Acipenser transmontanus). We assessed the vulnerability of juvenile rainbow trout (<1 year old), juvenile kokanee (Oncorhynchus nerka) (<1 year old), and two ages of white sturgeon (<1 year old and 3+ years old) to GBT. Bubble formation and the time to 50% loss of equilibrium (LOE) was quantified during exposure to nominal levels of 100%, 115%, 120%, and 130% TDG. We predicted that all three species would show similar times to 50% LOE at a given TDG level. However, time to LOE was longer, the proportion of fish with external symptoms of GBT was lower, and the proportion of fish with bubbles in the gills was higher or lower (dependant on age) in white sturgeon relative to rainbow trout and kokanee at a given TDG. The physiological basis for the difference is not known. However, it is important to consider species-specific differences in TDG sensitivity in the conservation of vulnerable species.
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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 | 0.001 |
| 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".