Effect of fruit maturity on the incidence of bitter pit, senescent breakdown, and other post-harvest disorders in ‘Honeycrisp’<sup>tm</sup>apple
Bibliographic record
Abstract
SummaryThis study examined the effect of harvest date (fruit maturity) on the occurrence of post-harvest disorders in ‘Honeycrisp’tm apple (Malus × domestica Borkh.). Fruit were sampled from six commercial orchards in the Annapolis Valley, Nova Scotia, Canada over two growing seasons and seven (in 2008) or nine harvest dates (in 2009). After 3 months storage in refrigerated air at 3.5ºC without delayed-cooling, soft scald, low temperature breakdown (LTB; also called soggy breakdown), bitter pit, and senescent breakdown developed in the fruit and the incidence of each varied with harvest date. Bitter pit developed in immature fruit and declined with harvest date, whereas senescent breakdown was non-existent in immature fruit and increased with harvest date. Minimum bitter pit plus senescent breakdown (combined) occurred at the mid-point of the harvest period (at approx. week-5), when internal ethylene was just beginning to appear in the fruit. An increase in fruit size (to ≥ 250 g) appeared to increase the occurrence of these two disorders. This study has shown that there is an optimum maturity (harvest date) in ‘Honeycrisp’tm apples, when the fruit are of sufficient size and colour to meet market requirements, with minimum risk of manifesting the two calcium-related disorders, bitter pit and senescent breakdown. The incidence of these two disorders may increase if fruit are picked earlier (bitter pit) or later (senescent breakdown), especially in larger fruit (≥ 250 g).
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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 | 0.001 |
| 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".