Tracking the Distribution and Risk of Tar Spot of Corn in Indiana from 2015 to 2022
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tar spot of corn ( Zea mays L.), caused by Phyllachora maydis, was first confirmed in the United States in 2015 in Illinois and Indiana but has since spread to a total of 20 states and Ontario and Quebec, Canada. Severe tar spot epidemics have caused unexpected and significant yield losses in corn in the Midwest. It is critical to document the movement and risk factors of this disease to develop effective management strategies. Tar spot distribution and disease intensity data were collected in Indiana from 2015 to 2022. Tar spot severity data were assessed from images of leaves submitted to the Purdue Pest and Diagnostic Laboratory from 2015 to 2018, and a statewide survey was conducted annually from 2019 to 2022. In each county, two or more corn fields were scouted for tar spot, percent field incidence and average leaf severity were documented, and county-level weather data were collected. In Indiana, tar spot severity was negatively correlated with temperature and positively correlated with precipitation during June and August but negatively correlated with July precipitation. High relative humidity (>90%) was also positively correlated with tar spot severity during June, July, and August. Fields in northern Indiana had the highest severity throughout the survey and have the highest risk for the disease in the future. Pockets of tar spot outbreaks indicate that once it is found locally, favorable environmental conditions of moderate temperatures and fluctuating periods of seasonal moisture may increase the risk of a severe epidemic in a field in future years.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it