Performance of five haskap (Lonicera caerulea L.) cultivars and the effect of hexanal on postharvest quality
Bibliographic record
Abstract
Growers are challenged to provide premium, ripe haskap fruit to market while limiting the negative attributes associated with over-ripening. Hexanal is an inhibitor of phospholipase D, an enzyme involved in membrane degradation, and has shown promise in extending the longevity of fruit. This study investigated the performance of a hexanal-based preharvest spray for enhancing the quality and shelf life of haskap fruit, an emerging crop in Canada. At two locations in 2015, 2016, and 2017, five cultivars were sprayed with a control treatment or a 0.02% hexanal formulation at 2–3 wk before harvest. Fruit was stored at 4 °C and assessed for quality at successive times. Although inconsistent, results suggest a hexanal spray may impart a small benefit to the postharvest quality of haskap fruit. Secondary objectives included describing important agronomic characteristics of haskap and assessing the potential of a hexanal preharvest spray for enhancing fruit retention. Hexanal did not reduce fruit drop and had no effect on quality at harvest. Fruit contained high levels of soluble solids and titratable acids, were very dark in colour, and did not degrade in quality as rapidly as softer fruit crops such as strawberry or raspberry.
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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 | 0.001 |
| 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".