Is metabolic rate a reliable predictor of growth and survival of brown trout (<i>Salmo trutta</i>) in the wild?
Bibliographic record
Abstract
In salmonids, there seems to be a positive correlation between standard metabolic rate and growth rate under artificial rearing conditions. Several recent studies have suggested that phenotypic correlations between physiological or behavioural traits and developmental or life history responses might be common when assayed in low-complexity habitats but rare in those with a high degree of spatiotemporal complexity. This study provides the first test of the connection between metabolic and growth rates of juvenile brown trout (Salmo trutta) in natural streams. In two out of four streams, there was no relationship between metabolic rates and subsequent growth, whereas in the two others, growth and metabolic rates were negatively correlated. Furthermore, survival rates were either unaffected or negatively correlated with metabolic rates. These results reveal complex relationships between metabolic rate, growth, and environmental variability and suggest that (i) in the wild, negative selection on high metabolic rates may result from both juvenile mortality and reduced growth rates, (ii) the conclusions derived from laboratory experiments are not directly applicable to natural populations, and (iii) the correlations between metabolic rate and growth can prove useful after selection of the appropriate spatial and temporal scales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".