Estimating maximum oxygen uptake of fishes during swimming and following exhaustive chase – different results, biological bases and applications
Bibliographic record
Abstract
The maximum rate at which animals take up oxygen from their environment (ṀO2,max) is a crucial aspect of their physiology and ecology. In fishes, ṀO2,max is commonly quantified by measuring oxygen uptake either during incremental swimming tests or during recovery from an exhaustive chase. In this Commentary, we compile recent studies that apply both techniques to the same fish and show that the two methods typically yield different mean estimates of ṀO2,max for a group of individuals. Furthermore, within a group of fish, estimates of ṀO2,max determined during swimming are poorly correlated with estimates determined during recovery from chasing (i.e. an individual's ṀO2,max is not repeatable across methods). One explanation for the lack of agreement is that these methods measure different physiological states, each with their own behavioural, anatomical and biochemical determinants. We propose that these methods are not directly interchangeable but, rather, each is suited to address different questions in fish biology. We suggest that researchers select the method that reflects the biological contexts of their study, and we advocate for the use of accurate terminology that acknowledges the technique used to elevate ṀO2 (e.g. peak ṀO2,swim or peak ṀO2,recovery). If the study's objective is to estimate the 'true' ṀO2,max of an individual or species, we recommend that pilot studies compare methods, preferably using repeated-measures designs. We hope that these recommendations contribute new insights into the causes and consequences of variation in ṀO2,max within and among fish species.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".