Insects as alternative hosts for phytopathogenic bacteria
Bibliographic record
Abstract
Phytopathogens have evolved specialized pathogenicity determinants that enable them to colonize their specific plant hosts and cause disease, but their intimate associations with plants also predispose them to frequent encounters with herbivorous insects, providing these phytopathogens with ample opportunity to colonize and eventually evolve alternative associations with insects. Decades of research have revealed that these associations have resulted in the formation of bacterial-vector relationships, in which the insect mediates dissemination of the plant pathogen. Emerging research, however, has highlighted the ability of plant pathogenic bacteria to use insects as alternative hosts, exploiting them as they would their primary plant host. The identification of specific bacterial genetic determinants that mediate the interaction between bacterium and insect suggests that these interactions are not incidental, but have likely arisen following the repeated association of microorganisms with particular insects over evolutionary time. This review will address the biology and ecology of phytopathogenic bacteria that interact with insects, including the traditional role of insects as vectors, as well as the newly emerging paradigm of insects serving as alternative primary hosts. Also discussed is one case where an insect serves as both host and vector, which may represent a transitionary stage in the evolution of insect-phytopathogen associations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".