Epigenetics, the Role of DNA Methylation in Tree Development
Bibliographic record
Abstract
During development of multicellular organisms, cells become differentiated by modulating different programs of gene expression. Cells have their own epigenetic signature which reflects genotype, developmental history, and environmental influences, and it is ultimately reflected in the phenotype of the cells and the organism. However, in normal development or disease situations, such as adaptation to climate change or during in vitro culture, some cells undergo major epigenetic reprogramming involving the removal of epigenetic marks in the nuclei followed by the establishment of a different new set of marks. Compared with animal cells, biotech-mediated achievements are reduced in plants despite the presence of cell polypotency. In forestry, any sustainable developments using biotech tools remain restricted to the lab, without progressing to the field for application. Such barriers in the translation between development and implementation need to be addressed by organizations that have the power to integrate these two fields. However, a lack of understanding of gene regulation is also to blame for this barrier. In recent years, great progress has been made in unraveling the control of gene expression. These advances are discussed in this chapter, including the possibility of applying this knowledge in forestry practice.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".