Application of 1-Methylcyclopropene in Fresh-cut/Minimal Processing Systems
Bibliographic record
Abstract
The application of 1-methylcyclopropene (1-MCP) in fresh-cut processing systems has been approached in three ways: 1) treatment of freshly harvested crop before longer-term storage after which the product is processed, 2) treatment of whole product just before processing, or 3) treatment of fresh-cut product immediately after processing. Results in the literature to date are quite variable in terms of whether 1-MCP treatment provides a benefit, no effect, or a negative effect on shelf life and quality retention of fresh-cut product. There are a number factors that impact the nature and extent of response to 1-MCP by fresh product and these include, but are not limited to, temperature of storage for fresh-cut product, condition of raw product, type of fruit or vegetable, cultivar, harvest maturity, duration of storage before cutting, and the 1-MCP treatment approach. A critical analysis, using existing published and unpublished data, provides a preliminary assessment of the impact of some of these factors. This analysis is intended to provide some insight into important considerations on the use of 1-MCP in fresh-cut processing systems and will guide researchers in considering experimental parameters for future work.
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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 | 0.000 | 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".