Glycerol loss to water exceeds glycerol catabolism via glycerol kinase in freeze-resistant rainbow smelt (Osmerus mordax)
Bibliographic record
Abstract
Rainbow smelt accumulate high amounts of glycerol in winter. In smelt, there is a predictable profile of plasma glycerol levels that starts to increase in November (<5 μmol/ml), peaks in mid-February (>200 μmol/ml), and thereafter decreases to reach the initial levels in the beginning of May. The aim of this study was to investigate the respective role of the two main mechanisms that might be involved in glycerol clearance from mid-February: 1) breakdown of glycerol to glycerol-3-phosphate through the action of the glycerol kinase (GK) and 2) direct loss toward the environment. Over the entire glycerol cycle, loss to water represents a daily loss of ∼10% of the total glycerol content of fish. GK activities were very low in all tissues investigated and likely have a minor quantitative role in the glycerol cycle. These results suggest that glycerol levels are dictated by the rate of glycerol synthesis (accelerated and deactivated during the accumulation and decrease stages, respectively). Although not important in glycerol clearance, GK in liver might have an important metabolic function for other purposes, such as gluconeogenesis, as evidenced by the significant increase of activity at the end of the cycle.
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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".