Evaluation of fish-injury mechanisms during exposure to turbulent shear flow
Bibliographic record
Abstract
Understanding the factors that injure or kill turbine-passed fish is important to the operation and design of the turbines. Motion-tracking analysis was performed on high-speed, high-resolution digital videos of juvenile salmonids exposed to a laboratory-generated shear environment to isolate injury mechanisms. Hatchery-reared fall chinook salmon (Oncorhynchus tshawytscha, 93128 mm in length) were introduced into a submerged, 6.35-cm-diameter water jet at velocities ranging from 12.2 to 19.8 m·s1, with a reference control group released at 3 m·s1. Injuries typical of turbine-passed fish were observed and recorded. Three-dimensional trajectories were generated for four locations on each fish released. Time series of velocity, acceleration, force, jerk, and bending angle were computed from the three-dimensional trajectories. The onset of minor, major, and fatal injuries occurred at nozzle velocities of 12.2, 13.7, and 16.8 m·s1, respectively. Opercle injuries occurred at 12.2 m·s1nozzle velocity, while eye injuries, bruising, and loss of equilibrium were common at velocities of 16.8 m·s1and above. Of the computed dynamic parameters, acceleration showed the strongest predictive power for eye and opercle injuries and overall injury level, and it may provide the best potential link between laboratory studies of fish injury, field studies designed to collect similar data in situ, and numerical modeling.
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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".