011 Emulsions of Edible Plant Oils Reduce Fruit Decay in `Delicious' Apples and `Ya Li' Pears
Bibliographic record
Abstract
Effects of 10% plant oils (corn, soybean, peanut, canola, sunflower, safflower, rape seed, linseed, and cottonseed), 100 mg·L -1 chlorine, or 100 mg·L- 1 chlorine plus 10% oil combinations on pathogen ( B. cinerea, P. expansum , or G. cingulata ) infection and fruit decay in `Delicious' apples and `Ya Li' pears were studied. None of the oils showed inhibition on spore germination of the three pathogens by in vitro test. In inoculated fruit, oil treatments did not affect incidence but reduced severity of decay after 6 months storage at 0 °C plus 7 days at 20 °C, but no difference was found among the oils at the same concentration. In non-inoculated fruit, oils reduced fruit decay to low levels (4%) even in the most severe season. Oils also maintained fruit quality attributes, reduced water loses, and controlled scald in apples and internal browning in pears. Chlorine reduced incidence but did not reduce severity in decayed fruit. Fruit first drenched with chlorine then dipped in oil emulsions without pathogen inoculation remained decay free, while control fruit developed 10% to 15% or 13% to 23% decay after 6 months at 0 °C plus 7 days at 20 °C in both apples and pears, respectively.
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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 | 0.000 |
| 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".