Influence of river speed on path selection by migrating adult sockeye salmon (<i>Oncorhynchus nerka</i>)
Bibliographic record
Abstract
We applied stereovideographic techniques to investigate path selection, ground speed, and swimming speed in adult sockeye salmon (Oncorhynchus nerka) ascending the Seton River, British Columbia. We tracked three-dimensional trajectories of salmon through 10 reaches and characterized the current profile at each site. At sites with relatively slow currents, the encountered river current speeds along the fish's trajectory were significantly lower than the mean current speed of the site. However, at higher current speed sites, fish experienced current speeds at or above the average current speed of the site and increased their ground speed through these sites. Observed in situ swimming speeds were 1.476.0 times greater than swimming speeds expected based on tailbeat frequency swimming speed predictive relationships established in flume studies. We conclude that (i) at sites with relatively slow or moderate current speeds, fish minimize exposure to high-speed currents to minimize energy expenditure, (ii) at sites with high-speed currents, fish may change their migration strategy, minimizing time spent searching for low current speed pathways and increasing their ground speed to expedite passage, and (iii) laboratory-derived predictive equations may only be appropriate for predicting in situ swimming costs at sites with moderate and linearly flowing currents.
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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 | 0.001 |
| 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".