<i>Tomato yellow leaf curl virus</i> differentially influences plant defence responses to a vector and a non‐vector herbivore
Bibliographic record
Abstract
Plants frequently engage in simultaneous interactions with diverse classes of biotic antagonists. Differential induction of plant defence pathways by these antagonists, and interactions between pathways, can have important ecological implications; however, these effects are currently not well understood. We explored how Tomato yellow leaf curl virus (TYLCV) influenced the performance of its vector (Bemisia tabaci) and a non-vector herbivore (Tetranychus urticae) occurring separately or together on tomato plants (Solanum lycopersicum). TYLCV enhanced the performance of B. tabaci, although this effect was statistically significant only in the absence of T. urticae, which adversely affected B. tabaci performance regardless of infection status. In contrast, the performance of T. urticae was enhanced (only) by the combined presence of TYLCV and B. tabaci. Analyses of phytohormone levels and defence gene expression in wild-type tomatoes and various plant-defence mutants indicate that the enhancement of herbivore performance (for each species) entails the disruption of downstream defences in the jasmonic acid (JA) pathway. For T. urticae, this disruption appears to involve antagonistic effects of salicylic acid (SA), which is cumulatively induced to high levels by B. tabaci and TYLCV. In contrast, TYLCV was found to suppress JA-mediated responses to B. tabaci via mechanisms independent of SA.
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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 | 0.000 | 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) | 0.001 | 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".