Lean, mean, lipolytic machines: lipid mobilization in rainbow trout during graded swimming
Bibliographic record
Abstract
The mobilization of mammalian lipid reserves is strongly stimulated during exercise to reach a maximum at moderate intensities, but the effects of swimming speed on fish lipolysis have never been quantified. Continuous infusion of 2-[3H]glycerol was used to measure Ra glycerol (=rate of appearance of glycerol or lipolytic rate) in rainbow trout kept at rest, or during graded exercise in a swim tunnel up to critical swimming speed (Ucrit). Results show that Ra glycerol is 1.67±0.18 µmol kg−1min−1 in control animals, and remains at a steady level of 1.24±0.10 µmol kg−1min−1 in exercising fish at all swimming intensities. Baseline lipolytic rate provides more than enough fatty acids from lipid reserves to accommodate all the oxidative fuel requirements for swimming at up to 2 body lengths per second (BL s−1), and more than 50% of the energy needed at Ucrit (3.4±0.1 BL s−1). Such “excess lipolysis” also means that trout sustain high rates of fatty acid reesterification. Maintaining steady lipolysis at rest and throughout graded swimming is strikingly different from mammals that stimulate Ra glycerol by 2 to 5-fold to support exercise. Instead trout act like “lipolytic machines” that do not modulate Ra glycerol even when their metabolic rate triples: a strategy that eliminates the need to increase lipolytic rate during exercise. This study also supports the notion that maintaining a high rate of reesterification (or triacylglycerol/fatty acid cycling) may be a mechanism widely used by ectotherms to achieve rapid membrane remodelling in variable environments.
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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".