Evaluation of a Dynamic Model for Primary Infections Caused by <i>Plasmopara viticola</i> on Grapevine in Quebec
Bibliographic record
Abstract
Downy mildew is major grape disease in several areas of the world. Recently, a dynamic model for primary infections of grapes by Plasmopara viticola, forecasting time of primary lesions emergence, was developed in Italy. The model simulates the development of predicted oospore cohorts during the primary infection period. The efficacy of this disease-cycle-based model was evaluated in eastern Canada by comparing the time of lesion emergence predicted by the model with field observations in 20 and 23 vineyards in 2008 and 2009, respectively. For each vineyard, one to 20 simulation runs were performed depending on the number of oospore cohorts expected to form, for a total of 545 simulations. The model evaluation was based on the true positive proportion (lesion emergence was predicted and observed) and the true negative proportion (lesion emergence was not predicted and not observed) which were 0.996, and 0.907, respectively. A total of 313 simulations resulted in no infection among which 284 corresponded to no lesion emergence. In only one situation, lesions were observed and not predicted by the model. On the contrary, in 29 simulations run, lesion emergence was predicted but not observed in the field. Further validation of this model is required, but the results of this study are encouraging and this model may be used to improve timing of fungicide sprays against P. viticola. Accepted for publication 17 November 2010. Published 26 January 2011.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".