Summer Pruning, an Eco-Friendly Approach to Controlling Bitter Pit and Preserving Sensory Quality in Highly Vigorous Apple cv. ‘Reinette du Canada’
Bibliographic record
Abstract
Summer pruning reduces vegetative growth in apple trees, but it could have an impact on fruit quality. This study analyzed the effects of summer pruning as an eco-friendly pre-harvest alternative to chemical growth regulation inputs on instrumental and sensory quality of highly vigorous apple cv. ‘Reinette du Canada’, which has been awarded with a Protected Designation of Origin label in two environments. The results showed that summer pruning affected the mineral content of the fruit. Summer pruning reduced bitter pit, but it did not negatively affect fruit weight nor any other instrumental characteristic during storage. Moreover, sensory quality or degree of liking were not affected by summer pruning. Thus, summer pruning could be an eco-friendly pre-harvest alternative to chemical treatments to improve quality in global terms of ‘Reinette du Canada’ apple cultivar, regardless of the location. This technique contributed to the decrease of bitter pit incidence, but did not decrease sensory quality nor degree of liking.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".