Known Distribution of the Soybean Cyst Nematode, Heterodera glycines, in the United States and Canada in 2020
Bibliographic record
Abstract
In the United States and Canada, the most damaging pathogen of soybean, Glycine max, is the soybean cyst nematode (SCN), Heterodera glycines. Plant health professionals working for universities and state and provincial departments of agriculture in the United States and Canada are queried periodically about counties and rural municipalities that are newly known to be infested with SCN in their states and provinces. Such a census was conducted in 2020, and the results were compared with results of the most recent survey, published in 2017. Between 2017 and 2020, 55 new SCN-infested counties were reported from 11 U.S. states. Also, 24 new SCN-infested counties and rural municipalities were identified in the Canadian provinces of Manitoba, Ontario, and Quebec. A map of the known distribution of SCN in these two countries was updated. The results reveal steady expansion of the distribution of SCN throughout the United States and Canada, and the pest almost certainly will continue to spread among and within soybean-producing areas of these countries in the future. Therefore, continued scouting and soil sampling for detection of new SCN infestations are warranted as the first step toward successfully managing the pathogen.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".