Diffuse Flesh Browning in ‘Honeycrisp’ Apple Fruit is Associated with Low Temperatures during Fruit Growth
Bibliographic record
Abstract
Multiple types of flesh browning can occur as storage disorders in ‘Honeycrisp’ apple ( Malus × domestica Borkh.) fruit. Predicting its occurrence is hindered by differing definitions of the types of browning, incomplete understanding of their etiologies, and difficulty in assessing harvest maturity of ‘Honeycrisp’ fruit. In 2013, of ‘Honeycrisp’ fruit grown, harvested over multiple weeks, and stored in Maine, Minnesota, Ontario, and Quebec, only the Quebec fruit developed diffuse flesh browning. A detailed comparison showed that the Quebec fruit differed in size, but not in other quality attributes, from fruit of the other locations. The Quebec fruit experienced lower temperatures during active fruit growth and were increasing in cell size up to harvest. Analyses of climate data from 2009 to 2015 indicated that accumulated growing degree-days (GDD) 50–60 day after full bloom (DAFB) could account for 31% of the variation in diffuse flesh browning, and seasonal GDD <500 are associated with a greater likelihood of injury. Fruit that exhibited diffuse flesh browning had higher magnesium and lower fructose levels than unaffected fruit. As these measurements were made after browning was assessed, the timing of the onset of these characteristics in relation to browning cannot be determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".