Can Epigenetics Guide the Production of Better Adapted Cultivars?
Bibliographic record
Abstract
As the global population continues to grow, food demand will be reaching levels which current agricultural practices cannot meet. This projected demand combined with the negative impacts of climate change on crop production calls for more careful breeding efforts to develop better adapted plants more tolerant to climate fluctuations. Fortunately, the development of molecular biology techniques like genome, transcriptome and epigenome sequencing now offer new approaches to help classical breeding meet these challenges. This review focuses on the potential of epigenetic approaches, particularly the creation of epigenetic markers (epi-markers) for guiding the selection process in breeding programs. Many studies have indeed successfully linked stable epigenetic modifications to different plant traits of interest but research on the applicability of using epi-markers in breeding programs is still scarce. This review emphasises the current progress that has been made with regards to the usefulness of epi-markers in selective plant breeding programs and the gaps in knowledge that still need to be addressed. It highlights the importance of pursuing research efforts to confirm the value of epi-markers for crop development in the years to come in order to meet the agricultural challenges of the 21st century.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".