Standard metabolic rate predicts growth trajectory of juvenile crucian carp (<i>Carassius auratus</i>) under changing food availability
Bibliographic record
Abstract
Phenotypic traits vary greatly within populations and can have a significant influence on aspects of performance. The present study aimed to investigate the effects of individual variation in standard metabolic rate (SMR) on growth rate and tolerance to food-deprivation in juvenile crucian carp (Carassius auratus) under varying levels of food availability. To address this issue, 19 high and 16 low SMR (individuals were randomly assigned to a satiation diet for 3 weeks, whereas another 20 high and 16 low SMR individuals were assigned to a restricted diet (approximately 50% of satiation) for the same period. Then, all fish were completely food-deprived for another 3 weeks. High SMR individuals showed a higher growth rate when fed to satiation, but this advantage of SMR did not exist in food-restricted fish. This result was related to improved feeding efficiency with decreased food intake in low SMR individuals, due to their low food processing capacity and maintenance costs. High SMR individuals experienced more mass loss during food-deprivation as compared to low SMR individuals. Our results here illustrate context-dependent costs and benefits of intraspecific variation in SMR whereby high SMR individuals show increased growth performance under high food availability but had a cost under stressful environments (i.e., food shortage).
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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 | 0.000 | 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) | 0.001 | 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".