Towards a better monitoring of seed ageing under<i>ex situ</i>seed conservation
Bibliographic record
Abstract
Long-term conservation of 7.4 million ex situ seed accessions held in agricultural genebanks and botanic gardens worldwide is a challenging mission for human food security and ecosystem services. Recent advances in seed biology and genomics may have opened new opportunities for effective management of seed germplasm under long-term storage. Here, we review the current development of tools for assessing seed ageing and research advances in seed biology and genomics, with a focus on exploring their potential as better tools for monitoring of seed ageing. Seed ageing is found to be associated with the changes reflected in reactive oxygen species and mitochondria-triggered programmed cell deaths, expression of antioxidative genes and DNA and protein repair genes, chromosome telomere lengths, epigenetic regulation of related genes (microRNA and methylation) and altered organelle and nuclear genomes. Among these changes, the signals from mitochondrial and nuclear genomes may show the most promise for use in the development of tools to predict seed ageing. Non-destructive and non-invasive analyses of stored seeds through calorimetry or imaging techniques are also promising. It is clear that research into developing advanced tools for monitoring seed ageing to supplement traditional germination tests will be fruitful for effective conservation of ex situ seed germplasm.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".