Measurement and relevance of maximum metabolic rate in fishes
Bibliographic record
Abstract
Maximum (aerobic) metabolic rate ( MMR ) is defined here as the maximum rate of oxygen consumption ( O 2max ) that a fish can achieve at a given temperature under any ecologically relevant circumstance. Different techniques exist for eliciting MMR of fishes, of which swim‐flume respirometry (critical swimming speed tests and burst‐swimming protocols) and exhaustive chases are the most common. Available data suggest that the most suitable method for eliciting MMR varies with species and ecotype, and depends on the propensity of the fish to sustain swimming for extended durations as well as its capacity to simultaneously exercise and digest food. MMR varies substantially (>10 fold) between species with different lifestyles ( i.e . interspecific variation), and to a lesser extent ( i.e . intraspecific variation). MMR often changes allometrically with body size and is modulated by several environmental factors, including temperature and oxygen availability. Due to the significance of MMR in determining aerobic scope, interest in measuring this trait has spread across disciplines in attempts to predict effects of climate change on fish populations. Here, various techniques used to elicit and measure MMR in different fish species with contrasting lifestyles are outlined and the relevance of MMR to the ecology, fitness and climate change resilience of fishes is discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".