Development of a Semiselective Medium for Detection of <i>Nalanthamala psidii</i>, Causal Agent of Wilt of Guava
Bibliographic record
Abstract
Guava wilt, caused by Nalanthamala psidii, has become an important disease of guava (Psidium guajava) in Taiwan since the 1970s. This study was conducted to develop a semiselective medium for detecting N. psidii in soil and in tissues of diseased guava trees. Among 9 carbon and 21 nitrogen compounds tested in a modified Czapek-Dox medium, the most effective carbon and nitrogen sources for mycelial growth of N. psidii were sucrose and glycine, respectively. Among eight fungicides tested, iprodione at 5 μg ml–1 and azoxystrobin at 1 μg ml–1 were the most effective fungicides for detection of N. psidii in artificially infested soil or in naturally infected guava debris. Based on the requirement for carbon and nitrogen sources and response to fungicides, a semiselective medium designated as modified sucrose-glycine semiselective medium (mSGSSM) was developed for isolation of N. psidii, using the modified Czapek-Dox medium containing 3% sucrose, 0.3% glycine, iprodione at 5 μg ml–1, azoxystrobin at 1 μg ml–1, streptomycin at 200 μg ml–1, and neomycin at 200 μg ml–1. Colonies of N. psidii on mSGSSM at 30°C for 5 to 10 days were white to orange with sparse aerial hyphae. N. psidii was detected more accurately and efficiently on mSGSSM than on other media, including potato dextrose agar, modified Nash-Snyder medium, and modified Czapek-Dox medium. This semiselective medium is effective in detection of N. psidii from various parts of diseased guava trees and in soil; therefore, it would be a useful medium for etiological, ecological, and epidemiological studies of guava wilt.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".