Bibliographic record
Abstract
For many animals, survival of severe environmental stress (e.g. to extremes of heat or cold, drought, oxygen limitation, food deprivation) is aided by entry into a hypometabolic state. Strong depression of metabolic rate, often to only 1-20% of normal resting rate, is a core survival strategy of multiple forms of hypometabolism across the animal kingdom, including hibernation, anaerobiosis, aestivation and freeze tolerance. Global biochemical controls are needed to suppress and reprioritize energy use; one such well-studied control is reversible protein phosphorylation. Recently, we turned our attention to the idea that mechanisms previously associated mainly with epigenetic regulation can also contribute to reversible suppression of gene expression in hypometabolic states. Indeed, situations as diverse as mammalian hibernation and turtle anoxia tolerance show coordinated changes in histone post-translational modifications (acetylation, phosphorylation) and activities of histone deacetylases, consistent with their use as mechanisms for suppressing gene expression during hypometabolism. Other potential mechanisms of gene silencing in hypometabolic states include altered expression of miRNAs that can provide post-transcriptional suppression of mRNA translation and the formation of ribonuclear protein bodies in the nucleus and cytoplasm to allow storage of mRNA transcripts until animals rouse themselves again. Furthermore, mechanisms first identified in epigenetic regulation (e.g. protein acetylation) are now proving to apply to many central metabolic enzymes (e.g. lactate dehydrogenase), suggesting a new layer of regulatory control that can contribute to coordinating the depression of metabolic rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".