Quantification of<i>Agrobacterium vitis</i>from Grapevine Nursery Stock and Vineyard Soil using Droplet Digital PCR
Bibliographic record
Abstract
Current detection methodologies for Agrobacterium vitis, causing crown gall of grapevines, are time intensive and lack the ability to quantify pathogen abundance in nursery stock and soil. Information on pathogen abundance is a key component to develop management strategies. The aim of this study was to develop a rapid and sensitive quantification assay for grapevine nursery stock and vineyard soil via droplet digital polymerase chain reaction targeting the virA gene. DNA isolated from roots of dormant grapevines originating from nurseries in Germany, California, and Ontario were tested for virA abundance. Bacterial numbers varied with grapevine origin; plants from California had the highest numbers. In addition, rhizosphere soil from two vineyards in the Okanagan valley in British Columbia was tested over a growing season. Sampling time during the season did not affect virA gene abundance. The older vineyard had higher soil A. vitis populations than the younger vineyard. The assay developed here has potential for use in national clean plant programs to prevent import of infected grapevine nursery stock and to test vineyard soil for abundance of the pathogen before planting.
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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) | 0.000 | 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".