Performance of LS97-1610בSpencer’ soybean recombinant inbred line population segregating for resistance to<i>Fusarium virguliforme</i>
Bibliographic record
Abstract
Clark, W. D., Reyes-Valdes, M. H., Bond, J. and Kantartzi, S. K. 2013. Performance of LS97-1610בSpencer’ soybean recombinant inbred line population segregating for resistance to Fusarium virguliforme . Can. J. Plant Sci. 93: 1179–1185. Sudden death syndrome (SDS) is a devastating disease in soybean which is caused by Fusarium virguliforme. Sudden death syndrome resistance is a quantitative trait; therefore, development of resistant varieties requires understanding of complex genetics and environmental effect. In this study, we aimed to characterize 94 F5:8recombinant inbred (RI) lines derived from a cross between a resistant line for SDS, LS97-1610 and a susceptible cultivar, ‘Spencer’ and to identify sources of resistance. The RI lines were evaluated for their relative resistance (RR) to SDS along with the parents in four different environments; two years (2009 and 2010) and two locations (Carbondale and Valmeyer, IL). Analysis of variance revealed significant interactions between genotype and environment in each year of experimentation and in 2-yr combined data. Broad-sense heritability was estimated by ANOVA results, and it was moderate (61%). These results indicate that SDS resistance is partially unpredictable due to environmental influence. Ten RI were identified to have a RR with no significant differences from LS97-1610 and two of them had a RR lower than resistant parent. These stable RI across environments were used for further evaluation in breeding programs. Additionally, data obtained from field evaluation can be used in combination with molecular data to study the effect of quantitative trait loci (QTL) with the environment. The identification of common QTL across environments with consistent expression is the ultimate goal of every marker-assisted selection program.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".