Antagonism of Plant Pathogens by Calotropis procera
Bibliographic record
Abstract
Phomopsis sojae and Sclerotinia sclerotiorum are responsible for stem and pod dryness and white mold in soybean. These pathologies directly affect the quality of seeds/grains and compromise the entire plant. The use of extracts from different plants has been the subject of research for the control of several phytopathogens. Calotropis procera is among botanical species that synthesize efficient compounds for biocontrol. In this context, the aim of this study was to evaluate the in vitro effect of C. procera aqueous extract on P. sojae and S. sclerotiorum. The experiment was carried out in completely randomized blocks in a 2 × 5 factorial scheme (two fungi and five extract concentrations 0%, 5%, 10%, 15% and 20%) with 4 replicates. C. procera aqueous extract concentrations were added to Petri dishes containing PDA. After 48 hours, the mycelial growth rate was evaluated. After seven days of incubation, the fungal colony area, sporulation, and germination of P. sojae and S. sclerotiorum were evaluated. There was significant interaction between fungi × extract concentrations (p < 0.05) for all variables analyzed. The mycelial growth rate of P. sojae was lower than that of S. sclerotiorum. The diameter of the P. sojae fungal colony was smaller than that of S. sclerotiorum when concentrations of 5%, 10% and 15% were used. As the extract concentration increased, fungi sporulation and germination reduced.
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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 | 0.001 |
| 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".