Expression Analysis of Glycerol Synthesis–Related Liver Transcripts in Rainbow Smelt (<i>Osmerus mordax</i>) Exposed to a Controlled Decrease in Temperature
Bibliographic record
Abstract
Rainbow smelt (Osmerus mordax) accumulate high glycerol levels to avoid freezing at subzero temperatures. Glyceroneogenesis is activated by low temperature and occurs in liver via a branch in glycolysis and gluconeogenesis. In this study, carbohydrate and liver transcript levels of 21 genes potentially associated with glycerol production were assessed during a controlled warm to cold transition. Smelt were held at 8°C (warm smelt; non-glycerol accumulating) or subjected to a controlled decrease in water temperature from 8° to 0°C (cold smelt; glycerol accumulating) and sampled at the end of the temperature decrease and 1 mo later. In cold smelt compared with warm smelt, liver glycogen levels were lower and phosphoglucomutase transcript levels were higher. Plasma glycerol levels were higher and increased over time in cold smelt; in cold smelt, liver phosphofructokinase and pyruvate dehydrogenase kinase transcript levels increased over time. These findings imply that glycerol production is being fueled by glycogen degradation and inhibition of pyruvate oxidation serves to channel metabolic flux toward glycerol as opposed to complete glycolysis. Plasma glucose and liver glucose-6-phosphatase transcript levels were higher. Lipoprotein lipase transcript levels were higher, suggesting enhanced lipid breakdown to fuel energy metabolism. Glutamine synthetase transcript levels were higher, perhaps to store nitrogen for biosynthesis in spring.
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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".