Reservoir Weed Hosts of Tomato Yellow Leaf Curl<i>Begomovirus</i>from Tanzania
Bibliographic record
Abstract
A survey was initiated to detect tomato yellow leaf curl virus (TYLCV) and identify its reservoir weed hosts in six regions (Arusha, Morogoro, Dodoma, Iringa, Kilimanjaro and Dar es Salaam) in Tanzania. Three farms were randomly selected in each region. Assessment of TYLCV incidence was done by relating the number of infected tomato plants to the total number of plants assessed along a diagonal in five quadrants measuring 4m ‐ 4m in size (one at each corner of the farm and one at the centre). Disease severity was scored on a scale of 0 to 4 (where 0 = no symptoms and 4 = very severe symptoms). Within and outside each farm, weeds showing TYLCV-like symptoms were collected and either squash-blotted, dot-blotted or both on nylon membranes. The membranes were hybridized with DIG-labelled probe synthesized for the detection of TYLCV from Sardinia (TYLCV-Sar) following standard protocols. Selected plant species were experimentally inoculated with screenhouse cultures of TYLCV representative isolates from the six regions using Bemisia tabaci to determine their host status. Results indicated that TYLCV incidence and severity were significantly higher (P = 0.05) in Dodoma region than the rest of the regions. In Iringa region, the incidence and severity of TYLCV were the lowest of all regions. TYLCV was detected in 12 of the 17 dot-blotted samples and in all the 21 squashed samples using the non-radioactively labelled riboprobes. Similarly, five plant species (Capsicum annuum, Datura stramonium, Lycopersicon esculentum, Nicotiana glutionsa and N. tabacum) tested in the screenhouse were infected by the six TYLCV isolates used. It is recommended that weeds within and outside tomato farms be removed to eliminate or reduce sources of virus inoculum. The dot and squash blot techniques are convenient for field detection of the virus, and are especially useful for the detection of early and latent infections so that management strategies can be initiated and implemented.
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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 | 0.000 |
| 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".