Omics for Improving Seed Quality and Yield
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
Seed-related traits such as seed size, germination, vigour, dormancy, biochemical composition, and stress resistance are critical to ensuring agricultural productivity and global food security, particularly in current scenarios of climate change and environmental unpredictability. This review examines the transformative potential of omics technologies, encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics, in enhancing our understanding of seed biology and its applications in crop improvement. Genomics and transcriptomics are key technologies in future plant breeding and gene editing to optimise seed yield and quality. We reviewed the role of metabolomic approaches in uncovering the molecular mechanisms behind seed germination, vigour, dormancy, and the proteomic advances to elucidate markers of seed quality, combining these omic technologies to decipher DOG1 as a marker of dormancy. Both biotic and abiotic stress resistance in seeds were reviewed from a multi-omics perspective to determine the best avenues for improving the resilience of seeds against drought, salinity and pathogens. Moreover, omics approaches have been reviewed to optimise plant–microbe interactions, particularly in enhancing symbiotic relationships within the soil microbiome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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