Methodology for Determining Relationships Between Inoculum Concentration of<i>Botrytis cinerea</i>and<i>Penicillium expansum</i>and Stem End Decay of Pear Fruit
Bibliographic record
Abstract
The objective of this research was to determine quantitative relationships between incidence of stem end decay of pear fruit and inoculum concentration of Botrytis cinerea and Penicillium expansum using dry conidia applied to pear fruit in a settling tower. Five concentrations of conidia were applied to pear fruit, fruit were stored at -1°C for 8 months, and stem end decay was evaluated. In addition, conidia were washed from the surface of inoculated fruit, and DNA was extracted and quantified with real-time polymerase chain reaction (PCR). The linear regression relationships between percent stem end gray mold and B. cinerea conidia per liter of air or per square centimeter of fruit surface were significant (P = 0.01). At the highest inoculum dose introduced into the settling tower, conidia per liter of air, conidia per square centimeter, and percent stem end gray mold at 8 months after inoculation were 12, 31, and 39, respectively for 2000 and 6, 33, and 67, respectively for 2001. Similarly, the linear regression relationships between percent stem end blue mold and P. expansum conidia per liter of air or per square centimeter of fruit surface were significant (P = 0.01 and 0.05, respectively). At the highest inoculum dose introduced into the settling tower, conidia per square centimeter and percent stem end blue mold at 8 months after inoculation were 39 and 26, respectively for 2000 and 66 and 23, respectively for 2003. Real-time PCR provided a rapid, quantitative measure of B. cinerea and P. expansum DNA on pear fruit surfaces. Because of possible year-to-year shifts in susceptibility of fruit to decay, disease incidence:inoculum dose relationships may be of most value compared within years rather than across years. This would facilitate comparison of decay risk among orchards in order to determine which fruit is most suitable for long-term storage.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".