Myosin expression levels and enzyme activity in juvenile spotted wolffish (<i>Anarhichas minor</i>) muscle: a method for monitoring growth rates
Bibliographic record
Abstract
The activity of glycolytic enzymes and the expression levels of myosin RNA was monitored in the white muscle of juvenile spotted wolffish (Anarhichas minor) reared under different temperature regimes. A group of individually tagged juvenile spotted wolffish was reared for 6 months at 4, 6, 8, and 12 °C. After the rearing trial, biopsy samples were taken from white muscle of each individual and the relationship between individual growth, enzyme activity, and myosin expression was investigated. A positive relationship between the activities of two glycolytic enzymes (pyruvate kinase and lactate dehydrogenase) and individual growth rate was observed. Using real-time polymerase chain reaction (PCR) and specially developed primers for myosin mRNA and 18S rRNA for spotted wolffish, we were able to detect differences in the relative myosin expression between experimental groups, and a positive relationship between myosin expression and specific growth rates was observed. These methods may be useful as an indicator of growth rate in wild fish and a fast and reliable indicator of growth potential under culture conditions. The method also has the potential to measure differences in white muscle synthesis in fish reared under variable environmental parameters and during different life history stages.
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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".