Association Between Weather Variables, Airborne Inoculum Concentration, and Raspberry Fruit Rot Caused by <i>Botrytis cinerea</i>
Why this work is in the frame
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Bibliographic record
Abstract
Botrytis fruit rot (BFR), one of the most important diseases of raspberry (Rubus spp.), is controlled primarily with fungicides. Despite the use of fungicides, crop losses due to BFR are high in most years. The aim of this study was to investigate the association between airborne inoculum, weather variables, and BFR in order to improve the management of the disease as well as harvest and storage decisions. Crop losses, measured as the percentage of diseased berries during the harvest period, were monitored in unsprayed field plots at four sites in three successive years, together with meteorological data and the number of conidia in the air. Based on windowpane analysis, there was no evidence of correlation between crop losses and temperature, vapor pressure deficit, wind, solar radiation, or probability of infection. There were significant correlations between crop losses and airborne inoculum and between crop losses and humidity-related variables, and the best window length was identified as 7 days. Using 7-day average airborne inoculum concentration combined with 7-day average relative humidity for periods ending 6 to 8 days before bloom, it was possible to accurately predict crop losses (R 2 of 0.86 to 0.89). These models could be used to assist with managing BFR, timing harvests, and optimizing storage duration in raspberry crops.
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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