Known Distribution of the Soybean Cyst Nematode, Heterodera glycines, in the United States and Canada Through 2023
Bibliographic record
Abstract
The soybean cyst nematode (SCN), Heterodera glycines Ichinohe, is a major yield-reducing pathogen of soybean (Glycine max [L.] Merrill) in the United States and Canada, causing twice as much yield loss annually as any other pathogen. Reports of new discoveries of SCN in areas of the United States and Canada that grow the crop can lead to greater awareness of the pathogen and increased efforts to scout for the nematode. Beginning in January 2024, university nematologists, plant pathologists, and agronomists as well as government plant health officials in soybean-producing states in the United States and provinces in Canada were queried about the known distribution of the nematode. This publication contains an updated map of the known distribution of SCN in the United States and Canada through 2023 and a listing of counties and rural municipalities where the nematode initially was identified between 2000 and 2023. In total, there were 31 counties in 10 states in the United States plus three counties total in Manitoba and Ontario and 10 rural municipalities in Quebec, Canada, in which SCN was first discovered between 2020 and 2023. The results show that the distribution of SCN in the United States and Canada continues to expand, and sustained scouting for the presence of the pathogen is warranted to facilitate management to reduce soybean yield losses.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".