The determination of standard metabolic rate in fishes
Bibliographic record
Abstract
This review and data analysis outline how fish biologists should most reliably estimate the minimal amount of oxygen needed by a fish to support its aerobic metabolic rate (termed standard metabolic rate; SMR). By reviewing key literature, it explains the theory, terminology and challenges underlying SMR measurements in fishes, which are almost always made using respirometry (which measures oxygen uptake, ṀO2 ). Then, the practical difficulties of measuring SMR when activity of the fish is not quantitatively evaluated are comprehensively explored using 85 examples of ṀO2 data from different fishes and one crustacean, an analysis that goes well beyond any previous attempt. The main objective was to compare eight methods to estimate SMR. The methods were: average of the lowest 10 values (low10) and average of the 10% lowest ṀO2 values, after removing the five lowest ones as outliers (low10%), mean of the lowest normal distribution (MLND) and quantiles that assign from 10 to 30% of the data below SMR (q0·1 , q0·15 , q0·2 , q0·25 and q0·3 ). The eight methods yielded significantly different SMR estimates, as expected. While the differences were small when the variability was low amongst the ṀO2 values, they were important (>20%) for several cases. The degree of agreement between the methods was related to the c.v. of the observations that were classified into the lowest normal distribution, the c.v. MLND (C.V.MLND ). When this indicator was low (≤5·4), it was advantageous to use the MLND, otherwise, one of the q0·2 or q0·25 should be used. The second objective was to assess if the data recorded during the initial recovery period in the respirometer should be included or excluded, and the recommendation is to exclude them. The final objective was to determine the minimal duration of experiments aiming to estimate SMR. The results show that 12 h is insufficient but 24 h is adequate. A list of basic recommendations for practitioners who use respirometry to measure SMR in fishes is provided.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".