Freeze Resistance in Rainbow Smelt (<i>Osmerus mordax</i>): Seasonal Pattern of Glycerol and Antifreeze Protein Levels and Liver Enzyme Activity Associated with Glycerol Production
Bibliographic record
Abstract
Rainbow smelt (Osmerus mordax) inhabit inshore waters along the North American Atlantic coast. During the winter, these waters are frequently ice covered and can reach temperatures as low as -1.9 degrees C. To prevent freezing, smelt accumulate high levels of glycerol, which lower the freezing point via colligative means, and antifreeze proteins (AFP). The up-regulation of the antifreeze response (both glycerol and AFP) occurs in early fall, when water temperatures are 5 degrees -6 degrees C. The accumulation of glycerol appears to be the main mechanism of freeze resistance in smelt because it contributes more to the lowering of the body's freezing point than the activity of the AFP (0.5 degrees C vs. 0.25 degrees C for glycerol and AFP, respectively) at a water temperature of -1.5 degrees C. Moreover, AFP in smelt appears to be a safeguard mechanism to prevent freezing when glycerol levels are low. Significant increases in activities of the liver enzymes glycerol 3-phosphate dehydrogenase (GPDH), alanine aminotransferase (AlaAT), and phosphoenolpyruvate carboxykinase (PEPCK) during the initiation of glycerol production and significant correlations between enzyme activities and plasma glycerol levels suggest that these enzymes are closely associated with the synthesis and maintenance of elevated glycerol levels for use as an antifreeze. These findings add further support to the concept that carbon for glycerol is derived from amino acids.
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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 | 0.001 | 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".