Epidemiological Survey of Grapevine Leafroll and Red Blotch Diseases in Baja California, Mexico
Bibliographic record
Abstract
<h3>Abstract</h3> <h3>Background and goals</h3> In Baja California, Mexico, the occurrence of grapevine leafroll (GLD) and grapevine red blotch (GRBD) diseases and the presence of the vine mealybug (vector of some grapevine leafroll-associated viruses) have been recognized. In this work, an epidemiological study was conducted to determine the prevalence and incidence in Baja California, Mexico, of symptomatic plants associated with both diseases. <h3>Methods and key findings</h3> Randomly-selected vineyards were surveyed to determine prevalence of GLD and GRBD, based on symptom evaluation in quadrats of 1000 grapevines. Results showed that 92% of sites had symptomatic plants, with an average prevalence of 20% (minimum = 0%, maximum = 92%). The main variables positively associated with disease prevalence were percentage of vine mealybug-infested plants per site, soil temperature, and accumulated heat units; ambient temperature was negatively associated with disease prevalence. Disease incidence determined during three growing seasons (2021 to 2023) in quadrats of 2500 grapevines in five vineyards revealed an average increase of 1.7% and 9% in 2022 and 2023, respectively. One site with the absence of vine mealybug showed the lowest percentage of symptomatic plants. Cabernet Sauvignon and Nebbiolo showed more symptomatic plants than Merlot. Virus infection in 137 symptomatic and 53 asymptomatic grapevines was tested by real-time PCR for grapevine leafroll-associated virus 1, -2, -3, and -4, and grapevine red blotch virus (GRBV). The diagnosis based on symptoms had a sensitivity of 90% and a specificity of 52%, with respect to real-time PCR results. <h3>Conclusions and significance</h3> The presence and widespread distribution of GLD and GRBD underscore the need to implement regional management programs in Baja California vineyards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 teacher head, 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".