The Big Rust and the Red Queen: Long-Term Perspectives on Coffee Rust Research
Bibliographic record
Abstract
Since 2008, there has been a cluster of outbreaks of the coffee rust (Hemileia vastatrix) across the coffee-growing regions of the Americas, which have been collectively described as the Big Rust. These outbreaks have caused significant hardship to coffee producers and laborers. This essay situates the Big Rust in a broader historical context. Over the past two centuries, coffee farmers have had to deal with the "curse of the Red Queen"-the need to constantly innovate in the face of an increasing range of threats, which includes the rust. Over the 20th century, particularly after World War II, national governments and international organizations developed a network of national, regional, and international coffee research institutions. These public institutions played a vital role in helping coffee farmers manage the rust. Coffee farmers have pursued four major strategies for managing the rust: bioprospecting for resistant coffee plants, breeding resistant coffee plants, chemical control, and agroecological control. Currently, the main challenge for researchers is to develop rust control strategies that are both ecologically and economically viable for coffee farmers, in the context of a volatile, deregulated coffee industry and the emergent challenges of climate change.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".