Blueberry and cranberry fruit composition during development
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
Compositional changes that occur during fruit development affect both the organoleptic and nutritional quality of small fruit. Compositional changes in blueberry (Vaccinium corymbosum L) and cranberry (Vaccinium macrocarpon Aiton) fruit were determined at 3 maturities (white, turning and fully colored) during 2 seasons by analyzing sugar, acid, total phenolic, and total anthocyanin composition, ORAC antioxidant capacity, and fruit firmness. In blueberry fruit, the primary sugars were glucose and fructose, which increased as fruit ripened. Citric acid comprised 77 to 87% of the organic acids in blueberry fruit. In addition, quinic and malic acids comprised 4 to 11% of total acids and small amounts of succinic, tartaric, and shikimic acids were present. Total acids declined 68% during fruit ripening. Total phenolics were greatest in white fruit and anthocyanins were greatest in blue fruit. Antioxidant capacity declined as fruit ripened from white to turning. Fruit firmness decreased about 80% as fruit ripened. In cranberry fruit, sugar concentration increased slightly as fruit ripened with glucose comprising about 80% of the total sugars. Acid content decreased 22% during ripening primarily due to a decline in citric acid. Quinic and malic acids increased slightly during ripening. Total anthocyanins increased as color developed, while total phenolics and antioxidant capacity remained relatively constant. In contrast to blueberries, red cranberry fruit were firmer than white or turning fruit.
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 | 0.001 | 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 | 0.001 |
| 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