Preharvest Spray Application of Blueberry Fruits with Hexanal Formulations Improves Fruit Shelf‐life and Quality
Bibliographic record
Abstract
Blueberry is one of the commercially valuable berry fruits. To meet the continuously increasing demand, there is a need to improve the postharvest storage technologies currently in use for this valuable crop. There are two types of blueberries, highbush and lowbush. Appearance of fruit (color, size, and shape), firmness or texture, and flavor (pH, acidity, and soluble solids) are the main quality indicators of blueberry fruit. One of the most important causes of postharvest decay on blueberries is gray mold rot, particularly following a wet season. Many preharvest and postharvest technologies are used currently to extend the postharvest life and preserve the quality of blueberry fruit. Blueberry fruits at white stage were subjected to four preharvest spray applications with formulations containing hexanal at weekly intervals. It is worth mentioning that preharvest treatment did not affect the general ripening process of blueberry and no adverse effects were noticed in appearance of 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.
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) | 0.002 | 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".