Relationship of Airborne <i>Botrytis cinerea</i> Conidium Concentration to Tomato Flower and Stem Infections: A Threshold for De-leafing Operations
Bibliographic record
Abstract
Gray mold, caused by Botrytis cinerea, is an important threat for tomato greenhouse producers. The influence of airborne conidia concentration (ACC) on both flower and stem-wound infections was studied in a greenhouse maintained at a temperature of 15, 20, or 25°C using diseased tomato leaves as the unique source of dry inoculum. Spore samplers were used to monitor ACC, and a previously developed real-time qPCR assay was used to quantify airborne B. cinerea conidia. The proportion of infected flowers remained low at ACC < 10 conidia/m3; above this concentration, flower infection increased with increasing ACC. The influence of ACC on proportion of infected flowers was well described by a sigmoid model (R2 = 0.90 to 0.92). The mean proportion of infected stem wounds over the three trials was 0.021; no infected wounds were observed at ACC < 100 conidia/m3. Based on logistic regression, the probability that a stem becomes infected increased rapidly with mean probabilities of 0.24 and 0.87 at ACCs of 315 and 3,161 conidia/m3, respectively. The results suggest that the amount of airborne B. cinerea inoculum in the greenhouse is often above the action threshold for flower infection and that monitoring airborne B. cinerea inoculum could help in timing de-leafing operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".