Sudden death syndrome of soybean in South America is caused by four species of <i>Fusarium</i> : <i>Fusarium brasiliense</i> sp. nov., <i>F. cuneirostrum</i> sp. nov., <i>F. tucumaniae</i>, and <i>F. virguliforme</i>
Bibliographic record
Abstract
Soybean sudden death syndrome (SDS) pathogens and dry bean root-rot pathogens were studied taxonomically, phylogenetically, and pathologically. Detailed phenotypic comparisons of macro- and microscopic features and phylogenetic analyses of multilocus DNA sequence data, including those on the nuclear ribosomal intergenic spacer region and the single copy nuclear gene translation elongation factor 1-a, indicated that they comprised five distinct species of Fusarium. Two new species causing soybean SDS in Brazil, F. brasiliense and F. cuneirostrum, are formally described. Fusarium cuneirostrum is responsible for soybean SDS in Brazil and dry bean or mung bean root-rot in the United States, Canada, and Japan. Strains of each species, including F. cuneirostrum isolates from dry bean and mung bean and F. phaseoli isolates from dry bean, were inoculated on soybean cultivar Pioneer 9492RR to determine their pathogenicity. Although intraspecific variation in pathogenicity was observed, all the species were able to induce typical SDS symptoms on soybean plants in the artificial inoculation tests. Comparisons of the key diagnostic morphological features reveal that all five species can be diagnosed using conidial morphology.
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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 | 0.001 | 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) | 0.001 | 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".