Soybean Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada, from 2015 to 2019
Why this work is in the frame
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Bibliographic record
Abstract
Soybean (Glycine max [L.] Merrill) yield losses as a result of plant diseases were estimated by university and government plant pathologists in 29 soybean producing states in the United States and in Ontario, Canada, from 2015 through 2019. In general, the estimated losses that resulted from each of 28 plant diseases or pathogens varied by state or province as well as year. Soybean cyst nematode (SCN) (Heterodera glycines Ichinohe) caused more than twice as much loss as any other disease during the survey period. Seedling diseases (caused by various pathogens), Sclerotinia stem rot (white mold) (caused by Sclerotinia sclerotiorum [Lib.] de Bary), and sudden death syndrome (caused by Fusarium virguliforme O’Donnell & T. Aoki) caused the next greatest yield losses, in descending order. Following SCN, the most damaging diseases in the northern United States and Ontario differed from those in the southern United States. The estimated mean economic loss from all soybean diseases, averaged across the United States and Ontario, Canada was US$45 per acre (US$111 per hectare). The outcome from the current survey will provide pertinent information regarding the important soybean diseases and their overall severity in the soybean crop and help guide future research and Extension efforts on managing soybean diseases.
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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