The Relationship between Epigenetic Changes and Seasonal Changes in Rabbits
Bibliographic record
Abstract
This study explores the close relationship between epigenetic changes and seasonal changes in rabbits. Through in-depth analysis of the epigenome of rabbits in different seasons, the researchers found that there are significant differences in the epigenome of rabbits in winter and summer, indicating that seasonal changes may be a key factor driving epigenetic changes in rabbits. one. Further research revealed the impact of seasonal changes on rabbit gene expression, showing that the expression levels of genes related to adaptation to low-temperature environments increased in the cold season, while other genes showed different expression patterns in the hot summer. Research limitations are mainly reflected in the limited geographical scope and sample size. Future research can understand the epigenetic changes of rabbits in different seasons from a more comprehensive perspective by expanding the research scope and introducing more regional data. In addition, future research can also be expanded to spring and autumn, as well as under different climate conditions, to gain a deeper understanding of the impact of seasonal changes on rabbit epigenetics. Finally, this research is not only of great significance to understanding rabbit biology, but also provides some useful inspirations for ecology and agriculture, and provides theoretical support for animal protection and animal husbandry.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".