Preharvest 1-Methylcyclopropene Treatment Reduces Soft Scald in ‘Honeycrisp’ Apples during Storage
Bibliographic record
Abstract
The main objective of this study was to investigate the effectiveness of preharvest 1-methylcyclopropene (1-MCP) treatment on the development of soft scald in ‘Honeycrisp’ apples. In addition, the effects of preharvest 1-MCP on fruit quality at harvest and after storage were examined. For two consecutive years of study, ‘Honeycrisp’ trees were sprayed preharvest with 1-MCP and fruit were harvested twice during each year. Preharvest 1-MCP treatments had little consistent effect on fruit maturity at the time of harvest. In both years of study, preharvest 1-MCP reduced the incidence of soft scald in ‘Honeycrisp’ apples after air storage at 0 or 3 °C for 5 or 6 months. Soggy breakdown developed only in the second year of study and high incidences were reduced by preharvest 1-MCP treatments. Preharvest 1-MCP often reduced flesh firmness loss in ‘Honeycrisp’ during storage, especially during the second year of study, and with 1-MCP application closer to harvest. Malic acid content was often higher in apples with the preharvest 1-MCP spray closer to harvest. Overall, the most important benefit of preharvest 1-MCP treatments on ‘Honeycrisp’ apples was the reduction in soft scald development. Due to the high potential for substantial fruit losses from this disorder, the use of preharvest 1-MCP sprays on ‘Honeycrisp’ apples could be very advantageous.
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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) | 0.002 | 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".