Assessment of Fungal Pathogens Affecting the Weed Conyza bonariensis in Argentina
Bibliographic record
Abstract
In the last years Conyza bonariensis has become an important weed and control is difficult with the use of current technology in Argentinean pampas region. The increasing prevalence of herbicide-resistant weed species, public concern related to pesticide use and the introduction of government policies for pesticide reduction, is driving the search for alternative methods to chemical control. The aims of the present study were to detect fungal diseases associated with C. bonariensis, to identify fungal isolates from the symptomatic leaves and to confirm through Koch’s postulates the isolates pathogenicity. Mycological analysis of symptomatic leaves showed the presence of twelve genera of filamentous fungi. Among 116 isolates, Colletotrichum spp. was the most prevalent genus followed by Nigrospora spp. and Septoria spp. In the pathogenicity assays, 22 out of 116 isolates were able to comply with the Koch’s postulates. The pathogenic isolates were included into three genera Alternaria spp., Colletotrichum spp. and Septoria spp. This study provides the first report that demonstrates pathogenicity of fungal isolates on C. bonariensis in Argentina and represents the first step in a future biocontrol 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".