Swimming performance of a pelagic species in the Yangtze River under different exposure modes of the total dissolved gas supersaturation
Bibliographic record
Abstract
During flood discharges of upstream dams in the Yangtze River, the pelagic fish have a stress risk from total dissolved gas (TDG) supersaturation in the river water. This study took the silver carp as the object and systematically evaluated the effects of TDG supersaturation levels and exposure time on their critical swimming speed (Ucrit) at different temperatures. The external symptoms of gas bubble disease were found when TDG levels exceeded 130%. Both exposure time and TDG level did not significantly impact the Ucrit of fish under 6 days of non-lethal exposure (110%, 120%, 130% TDG) with lower or higher water temperature. Significant differences in Ucrit were found among different exposure times at 11.0 ± 1.0°C under 10 hours of lethal exposure (135%, 140%, 150% TDG) and the Ucrit reduced by 59.88%, 83.32%, and 92.40%, respectively. TDG level had a significant impact on the Ucrit at 21.0 ± 1.0°C when exposure time exceeded 8 hours. Ucrit at 21.0 ± 1.0°C water were significantly greater than those at 11.0 ± 1.0°C water where conditions had the same TDG supersaturation and exposure time. Differences in Ucrit between temperatures ranged from 3.24 to 6.12 BL/s under non-lethal exposure and from 6.38 to 13.88 BL/s under lethal exposure. The results of this study can provide a reference for fish conservation during flood discharge.
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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".