Epigenetics in the modern era of crop improvements
Bibliographic record
Abstract
Epigenetic mechanisms are integral to plant growth, development, and adaptation to environmental stimuli. Over the past two decades, our comprehension of these complex regulatory processes has expanded remarkably, producing a substantial body of knowledge on both locus-specific mechanisms and genome-wide regulatory patterns. Studies initially grounded in the model plant Arabidopsis have been broadened to encompass a diverse array of crop species, revealing the multifaceted roles of epigenetics in physiological and agronomic traits. With recent technological advancements, epigenetic regulations at the single-cell level and at the large-scale population level are emerging as new focuses. This review offers an in-depth synthesis of the diverse epigenetic regulations, detailing the catalytic machinery and regulatory functions. It delves into the intricate interplay among various epigenetic elements and their collective influence on the modulation of crop traits. Furthermore, it examines recent breakthroughs in technologies for epigenetic modifications and their integration into strategies for crop improvement. The review underscores the transformative potential of epigenetic strategies in bolstering crop performance, advocating for the development of efficient tools to fully exploit the agricultural benefits of epigenetic insights.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".