Identification and Characterization of <i>Rhizoctonia</i> Species Associated with Soybean Seedling Disease
Bibliographic record
Abstract
In an effort to identify the Rhizoctonia spp. associated with seedling diseases of soybean, Rhizoctonia isolates were recovered from soybean seedlings with damping off and root and hypocotyl rot symptoms from Arkansas, Illinois, Kansas, Michigan, Minnesota, and the Canadian province of Ontario between 2012 and 2014. Based on cultural morphology, polymerase chain reaction restriction fragment length polymorphism, and phylogenetic analysis of the internal transcribed spacer (ITS) region of the ribosomal RNA genes, 80 isolates were confirmed to be Rhizoctonia solani, 24 were binucleate Rhizoctonia spp., and 10 were R. zeae. Of the 80 R. solani isolates, one belonged to anastomosis group (AG) 2-1, 52 belonged to AG-2-2IIIB, five belonged to AG-3 PT, three belonged to AG-4 HGI, two belonged to AG-4 HGIII, nine belonged to AG-7, and eight belonged to AG-11. Bayesian inference of phylogeny using the ITS region revealed two clades of R. solani AG-7 that possibly correspond to different AG-7 subgroups. Phylogenetic analysis also provided evidence for genetic relatedness between certain binucleate Rhizoctonia and some R. solani isolates. On 'Williams 82' soybean, isolates of AG-2-2IIIB were the most aggressive, followed by isolates of AG-7, AG-4, and AG-11. On 'Jubilee', a sweet corn cultivar, AG-2-2IIIB and AG-4 isolates caused significant stunting and root damage, whereas the damage caused by the AG-11 isolates was mostly restricted to the mesocotyl. Isolates of R. zeae and the binucleate Rhizoctonia spp. were not pathogenic on soybean or corn. Our results indicate that soybean and corn are hosts to the predominant and aggressive AG of R. solani, implying that rotation between these two crops may not be an effective management practice.
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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 | 0.001 |
| 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 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".