A procedure for quantifying adhesion of conidia of<i>Botrytis cinerea</i>to the skin of apple fruit
Bibliographic record
Abstract
Ultrasonication was evaluated as a nonchemical means to quantitatively remove conidia of Botrytis cinerea from the skin of Golden Delicious apple (Malus domestica Borkh.) fruit. A probe immersed in a suspension of conidia and generating 20 kHz at 150 W for 30- or 60-s pulses destroyed 13.3% or 29% of conidia, respectively. Destruction at 150 W for 10 s or at 30-120 W for up to 60 s was <2%. The procedure for quantifying adhesion of conidia to the skin of fruit consisted of pipetting a 50-microL water droplet containing 5 x 10(4) conidia onto the skinside of a slice of fruit, incubating the slices inside sealed 500 cm3 glass jars, excising a 1 cm diameter piece of skin bearing the droplet, and sonicating the skin in 8 mL of ice-cold water at 150 W for 10 s. The skin was removed, the suspension was centrifuged at 1250 x g for 15 min, and the supernatant was reduced to 1 mL by vacuum suction using a pipet. Conidia were stained with crystal violet and counted in a hemacytometer. Adhesion of conidia to skin was 3.0%, 14.6%, 20.8%, 39.4%, 57.6%, and 73.1% after 0, 2, 4, 8, 12, and 24 h incubation, respectively. Sonication was more effective than two other procedures for recovery of conidia. Conidia on the skin of fruit exposed to 4 microL of butyl acetate in the headspace of glass jars for 4 h at 23 degrees C increased the adhesion of conidia 107% above that for unexposed conidia. Sonication with a programmable power- and time-controlled probe was a simple, rapid, safe, and effective method for quantifying adhesion of B. cinerea conidia to the skin of apple fruit.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".