Towards a better monitoring of seed ageing under<i>ex situ</i>seed conservation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it