Changes in fruit skin <b><i>δ</i></b>Absorbance and heat stress response compounds in relation to sunburn browning severities of 'Buckeye Gala' apple
Bibliographic record
Abstract
High temperature deteriorates apple tree performance and fruit growth. In the summer of 2021, heat waves affected the apple production zones in the Pacific Northwest of North America. In a 'Buckeye Gala' experimental trial in Summerland, British Columbia, Canada, heat caused fruit skin sunburn browning (SB), and affected fruit mass, dry matter content, soluble solid content, titratable acidity, firmness and flesh water potential. We studied fruit skin δAbsorbance at 670–720 nm ( δA 670–720), cuticle characteristics, and key stress response compounds in peel and flesh, to elucidate the SB mechanism. SB was associated with thickened cuticle, chloroplast degradation, decreased anthocyanin content and increased firmness on the affected sunlit side of the fruit. As sunburn damage intensified, anthocyanin content in peel decreased, while the concentration of tartaric ester and total phenolics in flesh increased. The δA 670–720, measured using Delta Absorbance (DA) meter, was positively correlated with the total amount of phenolics and tartaric esters in peel and flesh. This suggested that the accumulation of the phenolics and tartaric ester triggered by heat stress contributed to the high δA 670–720 despite the degradation/absence of chloroplasts. The study improved our understanding of the impacts of sunburn severities on fruit quality and of the heat stress responses in 'Buckeye Gala' apple, and demonstrated the potential use of DA meter as a rapid, non-destructive tool in facilitating heat stress ecophysiology study and predicting SB severity in red apple varieties.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".