A Mechanistic Model Accounting for the Effect of Soil Moisture, Weather, and Host Growth Stage on the Development of <i>Sclerotinia sclerotiorum</i>
Bibliographic record
Abstract
The fungus Sclerotinia sclerotiorum causes serious losses to several agricultural crops worldwide. By using systems analysis, we retrieved the available knowledge concerning S. sclerotiorum from the literature and then analyzed and synthesized the data to develop a mechanistic, dynamic, weather-driven model for the prediction of epidemics on different crops. The model accounts for i) the production and survival of apothecia; ii) the production, dispersal, and survival of ascospores; iii) infection by ascospores; and iv) lesion onset. The ability of the model to predict the occurrence of apothecia was evaluated for epidemics observed with different climates, soil types, and host crops (soybean, white bean, and carrot) using independent data obtained from trials conducted in Ontario (Canada) in 1981, 1982, and from 1999 to 2002; in Michigan (U.S.A.) in 2015 and 2016; and in Wisconsin (U.S.A.) in 2016. The model showed 0.82 accuracy and 0.73 specificity in predicting the presence of apothecia, with a posterior probability of correctly predicting apothecia to be present or absent of 0.804 and 0.876, respectively. The model was also validated for its ability to predict disease progress on soybean and sunflower in Ontario in 1981 and 1982, in Manitoba (Canada) in 2001 and 2002, and in Michigan in 2015 and 2016. Comparison of model output with observations showed a concordance correlation coefficient of 0.948, and a root mean square error of 0.122. The model represents an improvement of previous S. sclerotiorum models and could be useful for making decisions on disease control.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".