Evaluation of fish-injury mechanisms during exposure to turbulent shear flow
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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·s 1 , with a reference control group released at 3 m·s 1 . 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·s 1 , respectively. Opercle injuries occurred at 12.2 m·s 1 nozzle velocity, while eye injuries, bruising, and loss of equilibrium were common at velocities of 16.8 m·s 1 and 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it