Cucurbit Downy Mildew ipmPIPE: A Next Generation Web-based Interactive Tool for Disease Management and Extension Outreach
Bibliographic record
Abstract
Cucurbit downy mildew (CDM), caused by Pseudoperonospora cubensis, is one of the most important diseases affecting cucurbits worldwide. In the USA, host resistance in cucumber had adequately controlled the disease with very minimal application of fungicides from the late 1960s to 2004. In 2004, there was a resurgence of the disease that devastated the cucumber crop in several states in the eastern USA. Since then, host plant resistance alone has not been sufficient to adequately control the disease and now control relies heavily on application of fungicides. To effectively apply fungicides in a timely manner, cucurbit growers, extension personnel, and crop consultants and advisors can now utilize information on disease occurrence and predicted spread disseminated through the United States Department of Agriculture's CDM ipmPIPE decision support system developed by scientists at North Carolina State University. Based on a survey conducted in Georgia, North Carolina, and Michigan, the CDM ipmPIPE resulted in an average reduction of 2 to 3 fungicide applications in 2009 compared to calendar-based fungicide sprays. With approximately 122,000 acres of cucurbits in these three states, this translates to more than $6 million in savings to the producers in these three states. Economic savings and positive environmental implications of reduced fungicide applications demonstrate the value of a coordinated national monitoring network for management of a plant disease that is disseminated aerially over long distances. Accepted for publication 31 January 2011. Published 11 April 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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".