The Complex Interactions of Common Bean (Phaseolus vulgaris L.) with Viruses, Vectors and Beneficial Organisms in the Context of Sub-Saharan Africa
Bibliographic record
Abstract
Common bean (Phaseolus vulgaris L.), the world’s most widely grown legume crop, is not only of great commercial importance but is also a vital smallholder crop in low-to-medium-income countries. In sub-Saharan Africa common bean provides consumers with a major proportion of their dietary protein and micronutrients. However, productivity is constrained by viruses, particularly those vectored by aphids and whiteflies, and problems are further compounded by seed-borne transmission. We describe common bean’s major viral threats including the aphid-transmitted RNA viruses bean common mosaic virus and bean common mosaic necrosis virus, and the whitefly-transmitted begomoviruses bean golden mosaic virus and bean golden yellow mosaic virus and discuss how high-throughput sequencing is revealing emerging threats. We discuss how recent work on indirect and direct viral ‘manipulation’ of vector behaviour is influencing modelling of viral epidemics. Viral extended phenotypes also modify legume interactions with beneficial organisms including root-associated microbes, pollinators and the natural enemies of vectors. While problems with common bean tissue culture have constrained transgenic and gene editing approaches to crop protection, topical application of double-stranded RNA molecules could provide a practical protection system compatible with the wide diversity of common bean lines grown in sub-Saharan Africa.
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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".