Modeling the Oxygen Consumption Rates in Pacific Salmon and Steelhead: Model Development
Bibliographic record
Abstract
Abstract We derived a series of models for estimating the standard metabolic rates, swimming costs, and total metabolic rates for sockeye salmon Oncorhynchus nerka and steelhead O. mykiss. The performance of these models was compared statistically and used to predict optimal cruising speeds. These predictions were tested with independent estimates of swimming speed obtained under field conditions. Standard metabolic rates were correlated with body mass and temperature. Swimming costs were correlated with body mass and swimming speed, whereas total metabolic rates were correlated with body mass, water temperature, and swimming speed. Swimming costs were also correlated with temperature and salinity in steelhead but not in sockeye salmon. Regression models accounted for 94–99% of the variance in standard metabolic rates, swimming costs, and total metabolic rates. The oxygen consumption rate models we derived for sockeye salmon were inadequate for describing oxygen consumption in other species of Pacific salmon, Oncorhynchus spp., indicating that the practice of borrowing parameters from closely related species can induce serious biases in model predictions. The models derived in this study also produced realistic estimates of swimming speed in sockeye salmon but not in steelhead. The models derived in this study appear to be useful in estimating swimming speed and total metabolic rates of sockeye salmon in the field but are not appropriate predictors for other species of Pacific salmon.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".