Effects of exercise training on carbohydrate and lipid catabolism in the swimming muscles of Nile tilapia (<i><scp>O</scp>reochromis niloticus</i>)
Bibliographic record
Abstract
This study aims to determine the effects of exercise training on carbohydrate and lipid catabolism in the swimming muscles of Nile tilapia (Oreochromis niloticus) by measuring the levels of related enzymes, lipids and free fatty acids. We designed one control group and two training groups of fish that were exercised at different training intensities [0, 1 and 1.5 body lengths per second (bl/s)]. The fish in the experimental groups were trained for 12 h/day for 4 weeks. Compared with the control group, the 1 and 1.5 bl/s groups showed significantly increased hexokinase and pyruvate kinase activities in red muscle (p < 0.05). In white muscle, pyruvate kinase activity was significantly higher in the 1.5 bl/s group than in the control group (p < 0.05), and hexokinase activity did not significantly differ between the groups. The activities of hormone-sensitive lipase and carnitine palmitoyltransferase I in both muscle types were significantly lower in the training groups than in the control group (p < 0.05). The plasma-free fatty acid level decreased (p < 0.05), while the lipid percentages increased in red muscle (p < 0.05) after exercise training. These findings clearly indicated that with exercise training, glycolysis increased and lipid oxidation decreased in the swimming muscle of tilapia.
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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".