Classification Methods and Identification of Reniform Nematode Resistance in Known Soybean Cyst Nematode-Resistant Soybean Genotypes
Bibliographic record
Abstract
Plant parasitic nematodes are a major yield-limiting factor of soybean in the United States and Canada. It has been indicated that soybean cyst nematode (SCN; Heterodera glycines Ichinohe) and reniform nematode (RN; Rotylenchulus reniformis Linford and Oliveira) resistance could be genetically related. For many years, fragmentary data have shown this relationship. This report evaluates RN reproduction on 418 plant introductions (PIs) selected from the U.S. Department of Agriculture Soybean Germplasm Collection with reported SCN resistance. The germplasm was divided into two tests of 214 PIs reported as resistant and 204 PIs reported as moderately resistant to SCN. The defining and reporting of RN resistance changed several times in the last 30 years, causing inconsistencies in RN resistance classification among multiple experiments. Comparison of four RN resistance classification methods was performed: (i) ≤10% as compared with the susceptible check, (ii) using normalized reproduction index (RI) values, and using (iii) transformed data log10(x), and (iv) transformed data log10(x + 1) in an optimal univariate k-means clustering analysis. The method of transformed data log10(x) was selected as the most accurate for classification of RN resistance. Among 418 PIs with reported SCN resistance, the log10(x) method grouped 59 PIs (15%) as resistant and 130 PIs (31%) as moderately resistant to RN. Genotyping of a subset of the most resistant PIs to both nematode species revealed their strong correlation with rhg1-a allele. This research identified genotypes with resistance to two nematode species and potential new sources of RN resistance that could be valuable to breeders in developing resistant cultivars.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".