Post-exercise respirometry underestimates maximum metabolic rate in juvenile salmon
Bibliographic record
Abstract
Abstract Experimental biologists now routinely quantify maximum metabolic rate (MMR) in fishes using respirometry, often with the goal of calculating aerobic scope and answering important ecological and evolutionary questions. Methods used for estimating MMR vary considerably, with the two most common methods being (i) the ‘chase method’, where fish are manually chased to exhaustion and immediately sealed into a respirometer for post-exercise measurement of oxygen consumption rate (ṀO2), and (ii) the ‘swim tunnel method’, whereby ṀO2 is measured while the fish swims at high speed in a swim tunnel respirometer. In this study, we compared estimates for MMR made using a 3-min exhaustive chase (followed by measurement of ṀO2 in a static respirometer) versus those made via maximal swimming in a swim tunnel respirometer. We made a total of 134 estimates of MMR using the two methods with juveniles of two salmonids (Atlantic salmon Salmo salar and Chinook salmon Oncorhynchus tshawytscha) across a 6°C temperature range. We found that the chase method underestimated ‘true’ MMR (based on the swim tunnel method) by ca. 20% in these species. The gap in MMR estimates between the two methods was not significantly affected by temperature (range of ca. 15–21°C) nor was it affected by body mass (overall range of 53.5–236 g). Our data support some previous studies that have suggested the use of a swim tunnel respirometer generates markedly higher estimates of MMR than does the chase method, at least for species in which a swim tunnel respirometer is viable (e.g. ‘athletic’ ram ventilating fishes). We recommend that the chase method could be used as a ‘proxy’ (i.e. with a correction factor) for MMR in future studies if supported by a species-specific calibration with a relevant range of temperatures, body sizes or other covariates of interest.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".