The Use of Vinegar Vapor to Reduce Postharvest Decay of Harvested Fruit
Why this work is in the frame
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Bibliographic record
Abstract
Vapors of several common vinegars containing 4.2% to 6.0% (= 2.5 to 3.6 mol·L -1 ) acetic acid effectively prevented conidia of brown rot [ Monilinia fructicola (G. Wint.) Honey], gray mold ( Botrytis cinerea Pers.:Fr.) , and blue mold ( Penicillium expansum Link) from germinating and causing decay of stone fruit ( Prunus sp.), strawberries ( Fragaria × ananassa Duchesne), and apples ( Malus × domestica Borkh.), respectively. Fruit were fumigated in 12.7-L sealed containers in which vinegar was dripped on to filter paper wicks or vaporized by heating from an aluminum receptacle. Vapor from 1.0 mL of red wine vinegar (6.0% acetic acid) reduced decay by M. fructicola on `Sundrop' apricots ( Prunus armeniaca L.) from 100% to 0%. Similarly, vapor from 1.0 mL of white vinegar (5.0% acetic acid) reduced decay in strawberries by B. cinerea from 50% to 1.4%. Eight different vinegars, ranging from 4.2% to 6.0% acetic acid, of which 0.5 mL of each vinegar was heat-vaporized, reduced decay by P. expansum to 1% or less in `Jonagold' apples. The volume of heat-vaporized white vinegar (5.0% acetic acid) necessary to reduce decay by P. expansum on `Jonagold' apples to zero was 36.6 μL·L -1 of air. Increasing the number of conidia on the apple surface reduced the effectiveness of vinegar vapor. The number of lesions caused by P. expansum on `McIntosh' apple decreased exponentially with increasing time of fumigation, approaching zero after about 6 hours. These results suggest that vinegar vapor could be an effective alternative to liquid biocides such as sodium hypochlorite for sterilization of surfaces contaminated by conidia of fungal pathogens.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it