Carbon utilization profiles of Fusarium virguliforme isolates
Bibliographic record
Abstract
Fusarium virguliforme is the cause of sudden death syndrome in soybean. Physiological variability among isolates of the fungus is unknown. One way to measure physiologic variability is to analyze growth on different carbon sources. The carbon source utilization profiles of 18 F. virguliforme isolates were examined using the Biolog FF 96-well microplate, which contains 95 different carbon sources. The utilization of dextrin,D-mannitol, maltotriose,D-lactic acid methyl ester, N-acetyl-D-galactosamine, salicin, D-trehalose, and L-alanine differed significantly among isolates (P = 0.05). Carbon sources were grouped into 3 clusters based on their ability to promote growth of F. virguliforme, after calculating Euclidean distances among them. About 12% of the carbon sources promoted a high amount of mycelial growth, 39% promoted a medium amount of growth, and 49% promoted a low amount of mycelial growth; the latter was not significantly different from the water blank control. A hierarchical tree diagram was produced for the 18 isolates based on their carbon source utilization profiles using Ward's hierarchical analysis method. Two main clusters of isolates were formed. One cluster represented greater average mycelial growth on all of the carbon sources than the other cluster. In this study, variability in carbon source utilization among F. virguliforme isolates was evident, but the results were not associated with geographic origin of the isolates, year collected, or published data on aggressiveness. Additional research is needed to determine if these carbon utilization profiles are associated with other biological characteristics, like spore germination, propagule formation, and saprophytic competitiveness.
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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".