Seed quality for conservation is critically affected by pre-storage factors
Bibliographic record
Abstract
The quality of seed-conservation collections, and hence their value for species reintroduction or restoration, is critically dependent on factors operating in the period between the point of collection and arrival at environmentally controlled processing and storage facilities. The timing of the acquisition of desiccation tolerance and seed longevity in air-dry storage, in relation to mass maturity and the time of natural seed dispersal, varies across species. In some wild plant species, seed quality continues to improve up to, and possibly beyond, the point of dispersal. Holding immature berries of Solanum dulcamara L. and capsules of Digitalis purpurea L. under natural conditions enabled comparison of seed quality between seeds stored under natural conditions and those dried rapidly under seedbank dry-room conditions. While seeds from fully ripe (post-mature) capsules of D. purpurea were insensitive to different post-harvest drying treatments, seed quality declined when mature berries of S. dulcamara were held under natural conditions. These results emphasise that the selection of post-harvest treatment will not only depend on the maturity of collected seeds but also may vary across species depending on the fruit type. Except for subtropical and tropical coastal locations, ambient daytime conditions during the main seed-collecting season (November–February) across Australia can be expected to result in tolerable rates of seed deterioration for the duration of seed-collecting missions. However, because seed moisture levels can be considerably higher than when equilibrated with ambient relative humidity, post-harvest handling decisions should ideally be informed by measurements of seed moisture at the time of collection, and subsequently seed moisture should be monitored during transit.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".