Development and validation of a disease forecast model for Sclerotinia rot of carrot
Bibliographic record
Abstract
Selected crop, microclimate and pathogen variables were monitored in carrot crops for four years to identify important variables associated with the development of Sclerotinia sclerotiorum and the start of epidemics of Sclerotinia rot of carrot. Soil moisture, and occasionally soil temperature, were the variables most closely associated with the development of apothecia and ascospores. Initial development of apothecia and ascospores occurred after one week of mean soil matric potential of −20 kPa or higher and maximum soil temperature up to 24 °C. At matric potentials of −30 to −40 kPa, development of apothecia and ascospores occurred in up to two weeks, and the occurrence of apothecia and ascospores was sporadic below −40 kPa. Preliminary risk algorithms were proposed to predict the occurrence of apothecia and ascospores, the start of epidemics, and the need for initial application of fungicides. Architectural and phenological stages of carrot development were used as primary risk factors incorporated into two predictive models. Ninety-five per cent closure of the carrot canopy was selected as a critical crop threshold to activate inoculum predictors. The critical crop thresholds to activate the disease forecasting system were 100% closure of the canopy plus 70 to 80% of plants with one to two collapsed senescing leaves and one to three healthy leaves lodged on the soil. The efficacy and accuracy of the model were tested over a two-year period. Applying the fungicide boscalid according to the forecast model resulted in equivalent disease control to using calendar-based sprays and decreased the number of fungicide applications in both years by up to 80%. Predicted inoculum values from the model were correlated with observed inoculum values at commercial field sites.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".