Bibliographic record
Abstract
Abstract The majority of foods consumed by humans and their domesticated animals as food sources are ultimately obtained from plants, especially seeds. The storage products in seeds are predominately carbohydrates, oils and proteins, which are synthesised and stored in specialised tissues during seed development. Ultimately the storage products ensure successful establishment of the new plant, and the vigour of the young seedling. For example, the reserves are utilised following germination to support early growth of the seedling, allowing it to survive before it commences photosynthesis and autotrophic growth. Some of the storage compounds of seeds play a direct protective role, allowing the seed to withstand water loss during the final stages of its development, and to survive in the dry state for long periods under adverse environmental conditions. Molecular, proteomic and other approaches are elucidating the regulatory networks of genes and encoded proteins that underlie the biochemical and physiological basis of seed maturation, and the accumulation of stored compounds. Key Concepts: Seed proteins directly provide more than half of the global intake of dietary protein in humans. In seeds, storage products (carbohydrates, oils and proteins) are accumulated during maturation and are utilised following germination to support early growth of the seedling. Likewise, storage products accumulated over winter in tree bark, tubers and perennial weed roots provide nutrients for rapid resumption of growth in the spring. There are regulatory networks of genes and encoded proteins that control the accumulation of stored compounds during seed maturation. Some of the storage products accumulated in seeds and plants are related to their longevity and protect them against environmental stresses. The genetic engineering of the storage products of seeds and plants is directed towards improving their nutritional, stability and food processing properties.
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.
How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".