Estimating the global severity of potato late blight with GIS‐linked disease forecast models
Bibliographic record
Abstract
Global severity of potato late blight was estimated by linking two disease forecast models, Blitecast and Simcast, to a climate data base in a geographic information system (GIS). The disease forecast models indirectly estimate late blight severity by determining how many sprays are needed during a growing season as a function of the weather. Global zonation of estimated late blight severity was similar for both forecast models, but Blitecast generally predicted a lower number of sprays. With both forecast models, there were strong differences between potato production zones. Zones of high late blight severity include the tropical highlands, western Europe, the east coast of Canada and northern USA, south‐eastern Brazil and central‐southern China. Major production zones with a low late blight severity include the western plains in India, where irrigated potato is produced in the cool dry season, north‐central China, and the north‐western USA. Using a global GIS data base of potato production, the average number of sprays was calculated by country. These averages were compared with estimates of current fungicide use. The results using Blitecast and Simcast were correlated but only Blitecast estimates correlated with observed data for developed countries. The estimated number of sprays, whether from Blitecast or Simcast, did not correlate with the observed number of sprays in developing countries, and in a number of developing countries the predicted optimal number of sprays was much higher than the actual number observed. In these countries, increased access to host resistance and fungicides could have a strong economic impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".