Corn Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada, from 2016 to 2019
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
Annual reductions in corn (Zea mays L.) yield caused by diseases were estimated by university Extension-affiliated plant pathologists in 26 corn-producing states in the United States and in Ontario, Canada, from 2016 through 2019. Estimated loss from each disease varied greatly by state or province and year. Gray leaf spot (caused by Cercospora zeae-maydis Tehon & E.Y. Daniels) caused the greatest estimated yield loss in parts of the northern United States and Ontario in all years except 2019, and Fusarium stalk rot (caused by Fusarium spp.) also greatly reduced yield. Tar spot (caused by Phyllachora maydis Maubl.), a relatively new disease in the United States, was estimated to cause substantial yield loss in 2018 and 2019 in several northern states. Gray leaf spot and southern rust (caused by Puccinia polysora Underw.) caused the most estimated yield losses in the southern United States. Unfavorable wet and delayed harvest conditions in 2018 resulted in an estimated 2.5 billion bushels (63.5 million metric tons) of grain contaminated with mycotoxins. The estimated mean economic loss due to reduced yield caused by corn diseases in the United States and Ontario from 2016 to 2019 was US$55.90 per acre (US$138.13 per hectare). Results from this survey provide scientists, corn breeders, government agencies, and educators with data to help inform and prioritize research, policy, and educational efforts in corn pathology and disease management.
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.
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