Summer fruitlet thinning enhanced quality attributes of Ambrosia™ apple at harvest and after 4 months of cold air storage
Bibliographic record
Abstract
Summer fruitlet thinning is implemented as a routine orchard practice to produce apple fruits with good quality. However, its impacts on the dynamics of fruit quality metrics during the growing season and in the postharvest storage, remain unclear. In this study, summer hand thinning on fruitlets of Ambrosia™ apple (Malus × domestica Borkh.) was conducted on two dwarfing rootstocks, Malling 9 (M.9) and Budagovsky (B.9), in an organic orchard and a conventional orchard under the semi-arid climate in Similkameen Valley, BC. Adequate thinning [(AT) in which 70% of fruitlets were removed] and light thinning [(LT) in which 30% of fruitlets were removed] were implemented in randomized plots in 8 wk after full bloom. Fruit development and dry matter content (DMC) were then monitored during the growing season; fruit quality was subsequently evaluated at harvest and after 4 mo of air storage at 0.5 °C. Relative to LT, AT enhanced fruit quality attributes in DMC, surface blush coverage and intensity, and soluble solids content (SSC) at harvest. The apples with higher DMC under AT also possessed higher compositional quality and lower incidences of fruit disorder in the postharvest stage. This study suggests that summer fruitlet thinning of Ambrosia apples can have significant impacts on fruit composition during subsequent on-tree fruit development, on the onset of ripening and eventually on the retention of quality and minimization of disorders over 4 mo of cold air storage. This effect is found for organic orchard (OG) and conventional orchard (CV) production systems and with both dwarfing rootstocks.
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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 | 0.000 |
| 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".