Effect of Sustainable Preharvest and Postharvest Techniques on Quality and Storability of High-Acidity ‘Reinette du Canada’ Apple
Bibliographic record
Abstract
Consumers are more disposed towards paying price premiums for sustainable food products. This fact is especially important for products with a PDO (Protected Designation of Origin) label, such as the high-acidity apple cultivar ‘Reinette du Canada’. However, ‘Reinette du Canada’ apple fruit is affected by two of the main post-harvest disorders in apple cultivars, bitter pit and senescent breakdown. As a means to achieve sustainable production of this high-quality apple cultivar, the effect of a sustainable preharvest technique, summer pruning, in combination with a zero-residue postharvest treatment allowed in organic farming on calcium-related disorders and quality in global terms in this PDO-labelled apple cultivar was assessed during storage. The combination of the two sustainable practices was an effective tool in diminishing senescent breakdown from 43% to 17% and to decrease bitter pit incidence by up to 21% (from 36% to 15%) throughout storage, especially in fruit with high susceptibility to this disorder during storage. Moreover, the overall acceptability was kept high (values of more than 3 on a 5-point scale) when applying this environment-friendly sustainable horticulture practice, which will not only reduce the synthetic pesticide input in the apple agroecosystem, but also contribute to producing fruit with zero residues.
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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 | 0.000 |
| 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".