Bibliographic record
Abstract
Given the prevailing challenge of evolving climatic conditions that pose detrimental consequences to cereal crops, the field of epigenetics has emerged as a highly auspicious and captivating realm of scientific investigation. Uninterrupted endeavors are underway to comprehensively grasp the paramount significance of epigenetic regulation in the realm of plant biology. Epigenetic modifications, primarily characterized by their inheritable nature and their capacity to manifest independently of DNA sequence alterations, intricately interplay with the regulation of gene expression, thus frequently influencing pivotal biological processes (Pikard and Scheid, 2014). Epigenetics is centered around the intricate regulatory mechanisms governing the temporal and spatial activation or repression of specific genes, whereas epigenomics encompasses the comprehensive investigation of widespread epigenetic modifications across a multitude of genes within individual cells or the entirety of an organism. Epigenetic changes during biotic and abiotic stresses are being explored in crops including cereals such as rice, maize, and wheat, with a view to use it for crop improvement (Kakolidou et al., 2021). During the last two decades, a number of studies have been conducted in cereals to understand the molecular mechanism behind stress resistance/tolerance. Although, it is now evident that epigenetic components, in addition to genetic components, play important roles in regulating genes involved in response to biotic and abiotic stresses (Guarino et al., 2022). However, compared to the vast information available on genetic mechanism of abiotic stresses, the knowledge on epigenetic mechanism is limited, especially in crops with a complex polyploid genome like wheat.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".