Fungal Pathogen Prevalence in Myristica fragrans Houtt. (Nutmeg) Nurseries: Insights from Central Sulawesi, Indonesia
Bibliographic record
Abstract
The Nutmeg tree (Myristica fragrans Houtt.)serves as a Multi-Purpose Tree Species (MPTS) in high demand due to its multitude of applications.Despite its potential, Indonesia's nutmeg productivity lags behind the global average, with a yield of only 98.9 kg per hectare.Among the various impediments to productivity, the limited expertise of local farmers in nutmeg cultivation and the prevalence of leaf diseases in nutmeg seedlings in nurseries are paramount.Leaf diseases can be lethal to the seedlings and significantly impact their quality, which, in turn, affects the growth and productivity of the mature plants.This study was conducted to assess the prevalence of leaf diseases in nutmeg nurseries and identify the causal pathogens.The average percentage of damage across all disease types was found to be as follows: leaf spot (3.95%), leaf blight (4.42%), leaf rust (7.27%), and powdery mildew (1.025%).Pathogenic fungi were identified as the causative agents, with Nigrospora sp.causing leaf spot, Rhizoctonia sp.causing leaf blight, Oidium tingitanium causing powdery mildew, and Pestalotia sp.causing leaf rust.The overall average intensity of pathogen attack was 6.52%, classified in the mild damage category.Our findings suggest that fungal pathogens predominantly cause leaf diseases in nutmeg seedlings.Therefore, effective microclimate management strategies should be adopted to mitigate the impacts of these diseases in nutmeg nurseries.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".