Soybean cyst nematode: Challenges and opportunities
Bibliographic record
Abstract
Soybean cyst nematode (SCN) is the primary pest responsible for yield losses of Glycine max. Management of SCN remains difficult in commercial soybean production due to the length of its biological cycle, frequent changes in population virulence, and ease of spread via infested soil. Effective management relies on crop rotation in combination with resistant cultivars, which have been derived from a limited germplasm base. Breeding for SCN resistance in soybean is difficult due to the quantitative nature of the trait, genetic variation within SCN populations, time required for phenotyping experimental soybean lines, and environmental factors affecting SCN reproduction. Quantitative trait loci associated with SCN resistance have been identified on 17 of the 20 soybean linkage groups, explaining 1–91% of the total phenotypic variation. Two major resistance genes, rhg 1 and Rhg 4, have been identified on linkage groups G and A2, respectively. Several minor resistance genes have been identified, but their importance varies with germplasm source and nematode race. Enhancement of SCN resistance in G. max may be achieved by interspecific hybridization with G. soja, the wild ancestor, or by engineering plants with candidate resistance genes such as Hs1 pro-1 . Key words: Genetic engineering, Glycine soja, soybean cyst nematode, molecular markers, resistance
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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 | 0.000 |
| 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 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".