Effects of food ration on SMR: influence of food consumption on individual variation in metabolic rate in juvenile coho salmon (<i>Onchorhynchus kisutch</i>)
Bibliographic record
Abstract
1. Consistency of differences in standard metabolic rate (SMR) between individual juvenile salmonids and the apparently limited ability of individuals to regulate their SMR has led many researchers to conclude that differences in individual SMR are fixed (i.e. genetic). 2. To test for the effects of food ration on individual performance and metabolism, SMR was estimated by measuring oxygen consumption using flow-through respirometry on individually separated young of the year coho salmon (Oncorhynchus kisutch) placed on varying food rations over a period of 44 days. 3. Results demonstrate that the quantity of food consumed directly affects SMR of juvenile coho salmon, independent of specific dynamic action (SDA, an elevation in metabolic rate from the increased energy demands associated with digestion immediately following a meal) and indicates that higher food consumption is a cause of elevated SMR rather than a consequence of it. Juvenile coho salmon therefore demonstrated an ability to regulate their SMR according to food availability and ultimately food consumption. 4. This study indicates that food consumption may play a pivotal role in understanding individual variation in SMR independent of inherent genetic differences. We suggest that studies involving SMR need to be cautious about the effects of intra-individual differences in food consumption in communal tanks or in different microhabitats in the wild as disproportionate food consumption may contribute more to variation in SMR than intrinsic (genetic) factors. 5. In general, our results suggest that evolutionary changes in SMR are likely a response to selection on food consumption and growth, rather than SMR itself.
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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 | 0.001 |
| 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".