Comparing tar spot epidemics in high-risk areas in the United States and Honduras
Bibliographic record
Abstract
Tar spot of corn is a disease that causes significant production losses in the Americas. However, the dynamics of tar spot epidemics in different countries is currently unknown. We assessed the temporal dynamics of tar spot epidemics from six efficacy trial experiments conducted in the United States (US) (three) and Honduras (three) from 2019 to 2021. Data collected corresponded to different canopy positions (lower, middle and upper). In all experiments and canopies, treatments contributed to reducing disease as compared to non-treated controls. In the US the time from disease onset (yons) to maximum disease level (ymax) was longer than in Honduras. In all experiments, the disease developed from the lower to the upper canopy. The logistic model described well the disease progression data in its linear and non-linear form. Overall, the linear rates (rL*) were lower in the US than in Honduras, but in the US the highest rates occurred in the upper canopy, while in Honduras they occurred in the lower canopy. Logistic non-linear rates were in general higher in the US than in Honduras. Multi-treatment meta-analysis and the effect size showed that yons, rL*, standardized AUDPC and ymax were higher in Honduras than in the US in the lower canopy and higher in the US than Honduras in the upper canopy. The best correlations occurred in the lower canopy in Honduras or the upper canopy in the US. The information generated in this study helped identify differences in epidemiological dynamics in high-risk areas in two countries.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".