Effects of Preconditioning and Fruit Maturity on the Occurrence of Soft Scald and Soggy Breakdown in ‘Honeycrisp’ Apples
Bibliographic record
Abstract
Preconditioning, holding fruit at 10, 17.5, or 21 °C temperatures for up to 7 days before placement in cold storage, was inconsistent in its effect on soft scald and soggy breakdown in ‘Honeycrisp’ apples in Maine and Ontario. In Ontario, 4 days of preconditioning at 21 °C increased soft scald in 1 year but had no effect in the next year. Five d of preconditioning at 10 °C reduced soft scald and had no effect on soggy breakdown in 1 year but reduced it the next year. In Maine, 5 days preconditioning at 17.5 °C was effective in reducing soft scald and/or soggy breakdown in 2002 to 2007 when starch index at harvest was 5.9 to 7.2. Seven days of preconditioning at 17.5 °C increased soggy breakdown with an early harvest in two orchards but only in one of two orchards with a later harvest. This same preconditioning had no effect on soft scald with the first harvest but reduced it with the second. In the next year, the same preconditioning treatment increased soft scald and soggy breakdown with an early maturity but had no effect with a later maturity in one orchard but not in fruit from another. Conditions during preconditioning and subsequent cold storage temperatures varied from previous recommendations, and this may be why preconditioning was not consistent in our studies and in some cases increased chilling disorders.
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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 | 0.001 |
| 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".