Measuring maximum oxygen uptake with an incremental swimming test and by chasing rainbow trout to exhaustion inside a respirometry chamber yields the same results
Bibliographic record
Abstract
Abstract This study hypothesized that oxygen uptake ( Ṁ O 2 ) measured with a novel protocol of chasing rainbow trout Oncorhynchus mykiss to exhaustion inside a static respirometer while simultaneously monitoring Ṁ O 2 ( Ṁ O 2chase ) would generate the same and repeatable peak value as when peak active Ṁ O 2 ( Ṁ O 2active ) is measured in a critical swimming speed protocol. To reliably determine peak Ṁ O 2chase , and compare to the peak during recovery of Ṁ O 2 after a conventional chase protocol outside the respirometer ( Ṁ O 2rec ), this study applied an iterative algorithm and a minimum sampling window duration ( i.e. , 1 min based on an analysis of the variance in background and exercise Ṁ O 2 ) to account for Ṁ O 2 dynamics. In support of this hypothesis, peak Ṁ O 2active (707 ± 33 mg O 2 h −1 kg −1 ) and peak Ṁ O 2chase (663 ± 43 mg O 2 h −1 kg −1 ) were similar ( P = 0.49) and repeatable (Pearson's and Spearman's correlation test; r ≥ 0.77; P < 0.05) when measured in the same fish. Therefore, estimates of Ṁ O 2max can be independent of whether a fish is exhaustively chased inside a respirometer or swum to fatigue in a swim tunnel, provided Ṁ O 2 is analysed with an iterative algorithm and a minimum but reliable sampling window. The importance of using this analytical approach was illustrated by peak Ṁ O 2chase being 23% higher ( P < 0.05) when compared with a conventional sequential interval regression analysis, whereas using the conventional chase protocol (1‐min window) outside the respirometer increased this difference to 31% ( P < 0.01). Moreover, because peak Ṁ O 2chase was 18% higher ( P < 0.05) than peak Ṁ O 2rec , chasing a fish inside a static respirometer may be a better protocol for obtaining maximum Ṁ O 2 .
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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 | 0.000 |
| 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 teacher head, 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".