Factors affecting health-beneficial compounds in lettuce
Bibliographic record
Abstract
Lettuce contains several health-beneficial compounds. Increasing the concentration of key health-beneficial compounds in lettuce has thus become an objective of several breeding programs and producers. In a first experiment, 38 genotypes of lettuces including crisphead, butterhead, romain, leaf lettuce, stem, Latin and wild species, were grown in greenhouses at two sites and concentrations of major flavonoids and phenolic compounds were quantified. In a second experiment, 6 cultivars of lettuce were grown in growth chambers with high (20°C night/28°C day) or control (14°C night/18°C day) temperatures to study the effect of heat stress on the concentration of health-beneficial compounds in lettuce. The results indicated that concentrations of total flavonoids, total phenolics, Ferric-Reducing Antioxidant Power (FRAP) and chicoric differed significantly among lettuce genotypes. Results at the two sites were highly correlated, thus selection at one site may be sufficient. Among the most commonly cultivated types, red leaf lettuces had the highest total flavonoids concentration at the maturity stage, followed by butterhead, and green leaf, while crisphead and batavia had the lowest concentrations. Health-beneficial compounds could be affected by heat stress in lettuce, however, different lettuce cultivars responded differently to the heat stress.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".