Horticultural Factors Affecting Antioxidant Capacity of Blueberries and other Small Fruit
Bibliographic record
Abstract
It is now widely held that the antioxidants contained in fruit and vegetables can provide protection against certain human degenerative conditions that are associated with oxygen free radical damage. This view is supported by epidemiological, in vitro, and more recently, in vivo evidence. Phenolics (polyphenolics) contribute substantially to the antioxidant complement of many small fruit species whose ripe fruit are red, purple or blue in color. Fruit containing high levels of phenolic antioxidants would be attractive to health conscious consumers, therefore optimization of production and processing factors affecting small fruit antioxidant capacity is desirable. In many small fruit crops, antioxidant activity [measured as oxygen radical absorbing capacity (ORAC)] is positively correlated with their content of anthocyanins and total phenolics. Genera, species, and genotypes vary with respect to phenolic content. Both annual and geographical factors appear to influence ORAC, although many years of study are needed to distinguish these effects from other biotic and abiotic factors that influence fruit phenolic content. Antioxidant capacity due to phenolics is decreased by food processing practices, such as heat or aeration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".