Physiological basis of metabolic trade-offs between growth and performance among different strains of rainbow trout
Bibliographic record
Abstract
Adaptive trade-offs define the trait combinations that differentiate taxa and allow coexistence along environmental gradients. To understand the physiological trade-offs associated with growth, we examined relationships among metabolic rate, digestive capacity, tissue energy content, and growth in juveniles of three strains of rainbow trout (Oncorhynchus mykiss) that differ in growth. Fish were reared under satiation, 1% of body mass per day, and complete food deprivation treatments to assess differences in performance and adaptive trade-offs along a gradient of resource availability. The fast-growing hatchery strain had higher standard metabolic rate (SMR), lower aerobic scope, and potentially lower maximum metabolic rates, suggesting that high growth trades off against a reduced capacity to do metabolic work. Trout with high growth rates also generally had larger gastrointestinal tracts, maximum food consumption, and growth efficiency. Results demonstrate (i) higher SMR of fast growers appears to be related to a greater investment in high-maintenance digestive tissue that supports rapid growth; (ii) growth appears to trade off against active metabolism; and (iii) selection on growth involves a suite of integrated physiological and anatomical traits that are affected by both genotype and environment (ration).
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".