Conquering the Frozen Frontier through Epigenetics: Red-Eared Slider Turtles’ Battle for Survival in Ice-Encased Ponds
Bibliographic record
Abstract
Red-eared slider turtles (Trachemys scripta) have a remarkable adaptation that allows them to withstand prolonged periods of anoxia in ice-locked ponds during Canadian winters.Their survival is characterised by metabolic rate depression (MRD) which prioritises energy to pro-survival pathways and minimalizes energy expensive pathways by suppressing gene expression.Amongst many biochemical processes, epigenetic histone lysine acetylation and methylation play crucial roles in regulating gene expression during MRD, but they remain uncharacterised in skeletal muscle of red-eared slider turtles.This thesis presents evidence of epigenetic controls on histone lysine acetylation and methylation in red and white skeletal muscle tissue of the red-eared slider turtles.Many enzymes and histone marks showed trends that were consistent with downregulation of gene expression during anoxia.Other proteins and histone marks exhibited unexpected trends in relative protein expression, changes that were attributed either to non-histone target roles or pro-survival pathways needed by the turtle to survive. PrefaceThis integrated M.Sc.thesis is composed of two main research papers that are currently awaiting
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".