Pathogenic Characterization of Pestalotiopsis grandis-urophylla Isolates Using Mycelial Suspension
Bibliographic record
Abstract
Eucalyptus species are among the most important forest crops in the world and can be affected by several pathogens, mainly by fungi of the genus Pestalotiopsis, which cause leaf spots. Studies aimed at the pathogenic characterization of Pestalotiopsis spp. from lesions of eucalyptus leaves in Brazil are still limited. The objective of this work was to evaluate the pathogenic potential of Pestalotiopsis grandis-urophylla isolates. For that, healthy leaves of adult Eucalyptus grandis ‘GG 100’ plants were inoculated with mycelial suspension of different P. grandis-urophylla isolates. The leaves inoculated with the pathogen were submitted to controlled conditions in a humid chamber in transparent acrylic gerbox boxes. Disease severity assessments were performed at 4, 6, 8 and 10 days after inoculation. Isolate E 72-04 had the highest area under the disease progress curve (AUDPC). Regarding the development of lesions, isolates E-72-02 and E-72-03 fit the polynomial model of the second degree, while isolate E-72-04 fit a linear model. The methodology tested reproduced typical symptoms of Pestalotiopsis and can be used as a parameter for new pathogenicity tests with this fungal genus.
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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 | 0.000 | 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) | 0.000 | 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".