Gas Supersaturation May Reduce the Survival of Yearling Chinook Salmon in the Lower Columbia River and Ocean Plume
Bibliographic record
Abstract
Unusually high flows in the Columbia River in 2011 raised total dissolved gas (TDG) levels in the river above the 120 percent legal limit imposed to prevent harmful impacts to aquatic organisms. This provided a unique opportunity to evaluate the effect on smolt survival. In-river (IR) migrating juvenile yearling Chinook released at Bonneville Dam with acoustic tags during periods when TDG exceeded 120 percent received estimated maximum exposures of 134 TDG. Subsequent daily survival rates in the lower river and plume were reduced by 0.06 per day (SE equals 0.01) and 0.15 per day (SE equals 0.05) relative to IR migrant fish released when TDG was less than 120 percent. Transported smolts (T) released 10-13 kilometers below Bonneville Dam had lower maximum exposure levels (126 percent) and experienced no difference in daily survival rates relative to unexposed smolts. River temperature levels and trends in turbidity and disease prevalence between releases of high and low exposure smolts were not consistent with the observed effects on survival rates. We conclude that smolts may suffer from chronic effects of elevated TDG exposure while migrating through the Columbia River and plume. Consideration should be given to measuring these survival losses in an explicit experimental framework that isolates possible confounding factors.
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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) | 0.001 | 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".