Detection of Multiple Potato Viruses in the Field Suggests Synergistic Interactions among Potato Viruses in Pakistan
Bibliographic record
Abstract
Viral diseases have been a major limiting factor threating sustainable potato (Solanum tuberosum L.) production in Pakistan. Surveys were conducted to serologically quantify the incidence of RNA viruses infecting potato; Potato virus X (PVX), Potato virus Y (PVY), Potato virus S (PVS), Potato virus A (PVA), Potato virus M (PVM) and Potato leaf roll virus (PLRV) in two major potato cultivars (Desiree and Cardinal). The results suggest the prevalence of multiple viruses in all surveyed areas with PVY, PVS and PVX dominantly widespread with infection levels of up to 50% in some regions. Co-infections were detected with the highest incidence (15.5%) for PVX and PVS. Additionally the data showed a positive correlation between co-infecting viruses with significant increase in absorbance value (virus titre) for at least one of the virus in an infected plant and suggested a synergistic interaction. To test this hypothesis, glasshouse grown potato plants were challenged with multiple viruses and analyzed for systemic infections and symptomology studies. The results obtained conclude that multiple viral infections dramatically increase disease epidemics as compared to single infection and an effective resistance strategy in targeting multiple RNA viruses is required to save potato crop.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Field survey of potato virus incidence and synergistic interactions in Pakistan.
It investigates potato virus infections and interactions, not research itself.
Plant pathology field survey of potato viruses and coinfection synergy.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".