Molecular Methods to Detect and Quantify Botryosphaeriaceae Inocula Associated With Grapevine Dieback in Australia
Bibliographic record
Abstract
Botryosphaeria dieback, caused by species of Botryosphaeriaceae, is an important grapevine trunk disease in Australia. Inocula produced by the pathogens are primarily dispersed by rain splash and wind and infect pruning wounds leading to cankers, dieback, and eventually death of vines. The objective of this study was to develop molecular tools to detect and quantify Botryosphaeriaceae inocula from the environment. These tools are essential for investigating spore dispersal patterns of Botryosphaeriaceae pathogens in Australian vineyards. DNA extraction protocols were evaluated and one modified protocol was found suitable for extracting Botryosphaeriaceae DNA from artificially and naturally inoculated Burkard volumetric spore sampler tapes. Multispecies primers and a hydrolysis probe for quantitative PCR (qPCR) were further developed to detect and quantify Botryosphaeriaceae inocula from environmental samples. Specificity tests showed that the multispecies primers were able to amplify the DNA of 10 Botryosphaeriaceae species (58 isolates) found in Australia while none of the 27 nontarget fungal species (90 isolates) tested were amplified. The qPCR assay was suitable for amplifying purified DNA, synthetic DNA fragments (gBlocks), and mixed DNA from spore trap tapes. The qPCR method developed in this study was shown to be rapid and sensitive in detecting Botryosphaeriaceae inocula from the environment using spore traps.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".