Does variable epigenetic inheritance fuel plant evolution?
Bibliographic record
Abstract
Epigenetic changes influence gene expression and contribute to the modulation of biological processes in response to the environment. Transgenerational epigenetic changes in gene expression have been described in many eukaryotes. However, plants appear to have a stronger propensity for inheriting novel epialleles. This mini-review discusses how plant traits, such as meristematic growth, totipotency, and incomplete epigenetic erasure in gametes promote epiallele inheritance. Additionally, we highlight how plant biology may be inherently tailored to reap the benefits of epigenetic metastability. Importantly, environmentally triggered small RNA expression and subsequent epigenetic changes may allow immobile plants to adapt themselves, and possibly their progeny, to thrive in local environments. The change of epigenetic states through the passage of generations has ramifications for evolution in the natural and agricultural world. In populations containing little genetic diversity, such as elite crop germplasm or habitually self-reproducing species, epigenetics may provide an important source of heritable phenotypic variation. Basic understanding of the processes that direct epigenetic shifts in the genome may allow for breeding or bioengineering for improved plant traits that do not require changes to DNA sequence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".