Optimal swimming speeds and forward-assisted propulsion: energy-conserving behaviours of upriver-migrating adult salmon
Bibliographic record
Abstract
Anadromous salmon migrations are energetically expensive. Long-distance migrants should be efficient in their use of energy and minimize swimming costs wherever possible. We explore swimming strategies and energy-saving tactics employed by three long-distance-migrating sockeye salmon (Oncorhynchus nerka) stocks in the Fraser River watershed, British Columbia. We used stereovideography and bank-side observations to estimate swimming speeds (from tailbeat frequency) and ground speeds (using distance traveled and duration) for individuals at several sites. Salmon were highly efficient at migration (i.e., ground speeds equaled or exceeded swimming speeds) through reaches with relatively low encountered currents (<0.25 m·s-1). We speculate that salmon exploit small reverse-flow vortices to achieve this feat. With low encountered currents, most salmon migrated according to an optimal swimming speed model: migrants minimized transport costs per unit distance traveled. Generally, salmon were less efficient at migration with fast currents, although the Chilko stock were superoptimal migrants, possibly owing to unique morphology and (or) behaviours. The risk of significant delays is enhanced when fast currents are encountered. Under these conditions, relatively fast swimming speeds could minimize travel time, despite high costs. Migrants may be balancing energetic costs of migration against the fitness costs of spawning delays.
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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".