Bibliographic record
Abstract
The wood frog, Rana sylvatica, has developed numerous adaptations to survive days with up to 65% of its body fluid frozen.One such adaptation is to reduce their metabolic rate, employing only those processes needed to survive until temperatures rise.The establishment of this hypometabolic state is mediated by transcriptional regulation that is elicited in part by histone methylation, however this has yet to be explored in the context of metabolic rate depression and freeze tolerance.This thesis provides the first characterization of histone methyltransferases (HMTs) and the histone and non-histone proteins they methylate in the wood frog.Transcriptionally permissive histone residues (H3K4me1 and H3K27me1) were found to decrease during freezing in skeletal muscle while those that silence transcription (H3K9me3 and H3K36me2) were maintained, whereas differential levels of histone residues were seen in liver.These findings suggest a novel role for HMTs in freeze tolerance.student I am today.I thank you for your mentorship, insights into the world of science, academia, and administration, and truly value your advice inside and out of the lab.I would also like to thank Jan Storey for her editorial review of this thesis and other documents.It is your hard work, knowledge, and patience, that ensures the lab runs as smooth and successful as it does.I also thank all the Storey lab members, both past and present, for creating such a positive and fun environment to work in every day.In particular I would like to thank Sanoji Wijenayake for mentoring me and guiding my project, Kama Szereszewski for introducing me to the world of western blotting, and Bryan Luu for teaching me necessary techniques for other and future projects.Of course, I would not be here today if it were not for my parents, brother, and sister.Mom and Dad, thank you for always supporting, encouraging
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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".