Finding the best estimates of metabolic rates in a coral reef fish
Bibliographic record
Abstract
Metabolic rates of aquatic organisms are estimated from measurements of oxygen consumption rates ( ) through swimming and resting respirometry. These distinct approaches are increasingly used in ecophysiology and conservation physiology studies; however, few studies have tested whether they yield comparable results. We examined whether two fundamental measures, standard metabolic rate (SMR) and maximum metabolic rate (MMR), vary based on the method employed. Ten bridled monocle bream (Scolopsis bilineata) were exercised using (1) a critical swimming speed (Ucrit) protocol, (2) a 15 min exhaustive chase protocol and (3) a 3 min exhaustive chase protocol followed by brief (1 min) air exposure. Protocol 1 was performed in a swimming respirometer whereas protocols 2 and 3 were followed by resting respirometry. SMR estimates in swimming respirometry were similar to those in resting respirometry when a three-parameter exponential or power function was used to extrapolate the swimming speed- relationship to zero swimming speed. In contrast, MMR using the Ucrit protocol was 36% higher than MMR derived from the 15 min chase protocol and 23% higher than MMR using the 3 min chase/1 min air exposure protocol. For strong steady (endurance) swimmers, such as S. bilineata, swimming respirometry can produce more accurate MMR estimates than exhaustive chase protocols because oxygen consumption is measured during exertion. However, when swimming respirometry is impractical, exhaustive chase protocols should be supplemented with brief air exposure to improve measurement accuracy. Caution is warranted when comparing MMR estimates obtained with different respirometry methods unless they are cross-validated on a species-specific basis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".