SEED DEVELOPMENT AFTER RECIPROCAL CROSSES BETWEEN DIPLOID AND TETRAPLOID RYE
Bibliographic record
Abstract
The application of next generation sequencing (NGS) technique has a great impact on epigenetic studies. Coupled with NGS, a number of sequencing-based methodologies have been developed and applied in epigenetic studies, such as Whole Genome Bisulfite Sequencing (WGBS), Reduced Representation Bisulfite Sequencing (RRBS), Methylated DNA Immunoprecipitation Sequencing (MeDIP-seq), Chromatin Immunoprecipitation-Sequencing (ChIP-seq), TAB-seq (Tet-assisted Bisulfite Sequencing), Chromosome Conformation Capture Sequencing (3C-seq) and various of 3C-seq de-rivatives, DNase1-seq/MNase-seq/FAIRE-seqand RNA Sequencing (RNA-seq). These new techniques were used to iden-tify DNA methylation patterns and a broad range of protein/nucleic acid interactions, and to analyze chromatin conforma-tion.With these new technologies, researchers have gained a broader view and better tools to investigate the distributions and dynamic changes of epigenetic markers affected by both internal and external factors. The principles and characteristics of major applications of NGS technologies on epigenetics were summarized; and the recent advances and the future direc-tions in NGS-based epigenetic studies were further discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".