The role of carbon sources in relation to pathogenicity of <i>Sclerotinia sclerotiorum</i> on Valencia peanut
Bibliographic record
Abstract
Sclerotinia sclerotiorum is a necrotrophic fungal pathogen with a very wide host range. Isolates of this pathogen are normally described as having fluffy and white mycelium; however, isolates of S. sclerotiorum with darkly-pigmented mycelium on potato dextrose agar medium have been identified in eastern New Mexico and western Texas from Valencia peanut fields. Mutant non-pigmented S. sclerotiorum isolates (SW) were created in an earlier study from wild-type pigmented isolates (SD) using melanin inhibitors. The SD isolates were pathogenic on Valencia peanut, whereas the SW isolates were not. The current study was conducted to further characterize the differences between SD and SW isolates in regards to metabolite production and utilization, including the effects of carbon sources and oxalic acid precursors on oxalic acid production and pathogenicity on Valencia peanut. Gas chromatography–mass spectrometry (GC/MS) metabolomics analysis revealed a down-regulation of several sugars and compounds within the citric acid cycle as well as oxalic acid for SW isolates of S. sclerotiorum. The addition of glucose to potato dextrose agar medium allowed for the production of oxalic acid and restored pathogenicity in SW isolates that were previously non-pathogenic on Valencia peanut. This study indicates that glucose alone plays a major role in oxalic acid production and pathogenicity of S. sclerotiorum.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".