The effect of turbulence on the cost of swimming for juvenile Atlantic salmon (<i>Salmo salar</i>)
Bibliographic record
Abstract
Fish activity costs are often estimated by transforming their swimming speed in energy expenditures with respirometry models developed while forcing fish to swim against a flow of constant velocity. Forced swimming models obtained using a procedure that minimizes flow heterogeneity may not represent the costs of swimming in rivers characterized by turbulence and by a wide range of instantaneous flow velocities. We assessed the swimming cost of juvenile Atlantic salmon (Salmo salar) in turbulent flows using two means (18 and 23 cm·s 1 ) and two standard deviations of flow velocity (5 and 8 cm·s 1 ). Twenty respirometry experiments were conducted at 15 °C with fish averaging 10 g. Our results confirmed that swimming costs are affected by the level of turbulence. For a given mean flow velocity, swimming costs increased 1.3- to 1.6-fold as turbulence increased. Forced swimming models under estimated actual swimming costs in turbulent flow by 1.9- to 4.2-fold. Spontaneous swimming models overestimated the real cost of swimming in turbulent flow by 2.8- to 6.6-fold. Our analyses suggest that models in which both the mean and the standard deviation of flow velocity are explicitly represented are needed to adequately estimate the costs of swimming against turbulent flows.
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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 | 0.001 |
| 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 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".