Relationships among postharvest ripening attributes and storage disorders in ‘Honeycrisp’ apple
Bibliographic record
Abstract
Introduction. The objective of this study was to examine the relationships among the ripening attributes of ‘Honeycrisp’ apples at harvest and after storage, and the direct and indirect contributions of these attributes to peel greasiness and the incidence of soft scald and soggy breakdown during storage using correlation and path-coefficient analyses. Materials and methods. Fruit were harvested from a commercial orchard at least five times throughout the commercial harvest period during four subsequent years (2008 to 2011). In two of the years, fruit were stored in air for 3 months at 3 °C and/or in a controlled atmosphere (1–2 kPa O2 + 1–2 kPa CO2) for 6 months at 3 °C. Fruit were analyzed at harvest and after storage. Results and discussion. Negative correlations were detected between internal ethylene concentration (IEC) and soluble solids concentration (SSC) or titratable acidity (TA) (the higher the IEC, the lower the SSC and TA) and a positive correlation between firmness and TA (the higher the firmness, the higher the TA). More peel greasiness and higher incidence of soggy breakdown during storage were associated with lower firmness, SSC and TA. Negative correlations were also detected between the incidence of soft scald and IEC or peel greasiness. The results of the path-coefficient analyses suggest that, in ‘Honeycrisp’, interrelationships among postharvest ripening indices and each individual disorder differ. Three possible path models for the interrelationships among ripening attributes (independent variables) and the incidence of peel greasiness, soft scald and soggy breakdown (dependent variables) are presented.
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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 | 0.001 | 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".