Cell cycle and germination of fresh, dried and deteriorated sugarbeet seeds as indicators of optimal harvest time
Bibliographic record
Abstract
Abstract Seeds of sugar beet (Beta vulgarisL.) were collected at weekly intervals from 3 weeks before to 1 week after commercial harvest time, dried and stored at room temperature (18–22°C). Laboratory germination tests and flow cytometric analyses were performed immediately after harvest (fresh seeds) and five times at weekly intervals during storage (dry seeds). After 6 months of storage, seeds were exposed to a controlled deterioration treatment (CD). The proportion of G2nuclei in the embryo was constant in the fresh seeds, regardless of their maturity. It decreased, however, after drying and CD, especially in those seeds harvested before maturation drying had commenced. The proportion of endosperm cells in the seed decreased with maturation, and a further decrease was observed after drying and CD. These observations suggest that nuclei with a higher nuclear DNA content were more sensitive to water stress caused by premature desiccation and to deterioration than nuclei with a lower DNA content. Fresh seeds exhibited some germination, but this increased after drying, suggesting that desiccation induced a switch from the developmental to the germination mode. Germination percentages were the highest in dry seeds collected at the commercial harvest time and a week after. This high germinability coincided with the highest proportion of G2cells in the embryo. It is concluded that flow cytometry provides information about the status of sugarbeet seed maturation, seed quality and storage potential, and can be used for estimation of optimal harvest time.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".