Bibliographic record
Abstract
Seed dormancy is an adaptive trait that improves survival of the next generation by optimizing the distribution of germination over time. The agricultural and forest industries rely on seeds that exhibit high rates of germination and vigorous, synchronous growth after germination; hence dormancy is sometimes considered an undesirable trait. The forest industry encounters problems with the pronounced dormancy of some conifer seeds, a feature that can lead to non-uniform germination and poor seedling vigor. In cereal crops, an optimum balance is most sought after; some dormancy at harvest is favored because it prevents germination of the physiologically mature grain in the head prior to harvest (that is, preharvest sprouting), a phenomenon that leads to considerable damage to grain quality and is especially prominent in cool moist environments. The sesquiterpene abscisic acid (ABA) regulates key events during seed formation, such as the deposition of storage reserves, prevention of precocious germination, acquisition of desiccation tolerance, and induction of primary dormancy. Its regulatory role is achieved in part by cross-talk with other hormones and their associated signaling networks, via mechanisms that are largely unknown. Quantitative genetics and functional genomics approaches will contribute to the elucidation of genes and proteins that control seed dormancy and germination, including components of the ABA signal transduction pathway. Dynamic changes in ABA biosynthesis and catabolism elicit hormone-signaling changes that affect downstream gene expression and thereby regulate critical checkpoints at the transitions from dormancy to germination and from germination to growth. Some of the recent developments in these areas are 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".