Cryopreservation of <i>Fagus crenata</i> seeds: estimation of optimum moisture content for maintenance of seed viability by Bayesian modeling
Bibliographic record
Abstract
Fagus crenata Blume is an important forestry tree species that plays a key biological role in natural forests. Because F. crenata forests are declining under global warming, the conservation of seed resources is necessary to maintain sustainable regeneration. We examined the capability of seed cryopreservation to achieve semi-permanent preservation of the F. crenata genetic resource. Seeds at 7%–26% moisture content (MC) exhibited greater than 64% viability, with developing radicles growing at 22 °C in 16 h photoperiods following chilling at 2 °C for 2 months. These seeds were then stored cryogenically at –170 °C for 6 months; cryopreservation at 26% MC reduced seed viability to 12%, whereas dry seeds (7%–14% MC) maintained viability and were comparable with seeds that had neither been dried nor cryopreserved. Drying seeds to 6% MC reduced viability to 47%; following cryopreservation, the viability of these seeds with such a low MC was 36%. Using a Bayesian model, we then determined that the optimal range of MC for the cryopreservation of F. crenata seeds was 9%–12%. We conclude that cryopreservation is feasible for the long-term storage of F. crenata seeds, drying of the seeds to the range of optimal MC retains high viability during 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".