Relationship between food availability, glycerol and glycogen levels in low-temperature challenged rainbow smelt <i>Osmerus mordax</i>
Bibliographic record
Abstract
Rainbow smelt Osmerus modax accumulate glycerol in winter that serves as an antifreeze. Fish were held at 8 degrees C, or subjected to a decrease in water temperature to -1 degrees C over a 19 day period, and subsequently maintained at -1 degrees C from 15 January to 11 May 2004. Starved fish did not survive the challenge of temperature decrease, with death ensuing above the typical freeze point for marine teleosts (-0.8 degrees C). A decrease in temperature activates the glycerol accumulation mechanism at about 5 degrees C with peak plasma levels exceeding 300 micromol ml(-1). Glycerol levels begin to decrease in late February even at water temperatures below -1 degrees C, suggesting either an inherent circannual or photoperiod trigger, possibly in association with sufficiently high levels of antifreeze protein. Glycogen levels in liver did not change significantly in starved fish maintained at 8 degrees C. However, liver glycogen was depleted in fish subjected to the low-temperature challenge and at a faster rate in starved than in fed fish. Stored glycogen in liver and other tissues can account for only a small amount of the total glycerol production, suggesting a strong requirement for food during accelerated glycerol production. Liver glycogen levels increased in April and May in association with the decrease in glycerol. Levels of glycerol in liver, kidney, spleen, gill, intestine, heart, muscle and brain follow the same pattern as that in plasma. During the early part of the glycerol accumulation phase, all tissues except for liver have lower levels of glycerol in the intracellular space than the levels in plasma. In liver, glycerol is in equilibrium between the two compartments.
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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".