POST-HARVEST EMBRYO DEVELOPMENT IN GINSENG SEEDS INCREASES DESICCATION SENSITIVITY AND NARROWS THE HYDRATION WINDOW FOR CRYOPRESERVATION.
Bibliographic record
Abstract
BACKGROUND: Despite its self-pollinating characteristics, Korean ginseng germplasm is mainly maintained in clonal gene banks as there is no defined approach to the long-term conservation of its seed, including the most appropriate stage of embryo development for storage. OBJECTIVE: The aim of this study was to reveal the effect of embryo development on desiccation tolerance and cryopreservation success in ginseng seeds. MATERIALS AND METHODS: Seeds of Korean ginseng (Panax ginseng C.A. Meyer) at three post-harvest stages (immediately after harvesting and following treatments to enable internal growth of the embryo) were desiccated and cryopreserved. RESULTS: The hydration window for the >80% dehiscence and germination of cryopreserved ginseng seeds varied with embryo developmental stage: 3-9% moisture content (MC) for both unpulped and undehisced seeds when the embryo was 0.1 the length of the endosperm, 7-10% MC for dehisced seeds (0.5 embryo:endosperm) and 9-11% MC for seeds with fully developed embryos (0.9 embryo:endosperm). Whilst dried (4-8% moisture content) and undehisced seeds within fruits (unpulped seeds) lost more than half their viability during 1 year's storage at room temperature, cryopreservation enabled germination levels of c. 90%. Overall, 432 accessions of Korean ginseng landraces have been cryopreserved using undehisced seeds with or without fruits. CONCLUSION: Post-harvest treatment of Korean ginseng seeds to enable embryo development decreases tolerance of very low MCs, and thus narrows the hydration window for cryopreservation. Fresh-harvested and unpulped seeds that have been dried to c. 5% MC are recommended for long-term cryogenic storage.
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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) | 0.002 | 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".