Designer fruits and vegetables with enriched phytochemicals for human health
Bibliographic record
Abstract
High dietary intake of fruits and vegetables rich in phytochemicals, particularly those with antioxidant activity, has been linked to reduced risks of many chronic diseases including cancer and cardiovascular diseases. Nutraceuticals containing such bioactive phytochemicals have been popular and made available in the market. However, excessive supplementation with these extracted and sometimes purified phytochemicals may pose new health concerns. Non-processed fruits and vegetables that are known to be rich in bioactive phytochemicals are therefore advantageous for the intact and balanced phytochemical contents. However, production of phytochemicals in fruits and vegetables is affected by many factors. Genotype is a fundamental factor that affects the biosynthesis of phytochemicals, but farming practices and environmental factors such as geographic location, growing season, soil type and mineral status, plant maturity, postharvest storage and processing, can all significantly affect the concentration of many phytochemicals. Development of fruits and vegetables with elevated concentrations of known antioxidant phytochemicals must consider all these factors. This is a challenging task and requires close multi-disciplinary collaborations among scientists. Key words: Phytochemicals, antioxidant, fruits, vegetables, polyphenols flavonoids
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 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 | 0.000 | 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) | 0.011 | 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".