Trends in common bean breeding research aimed at resistance to common bacterial blight
Bibliographic record
Abstract
Brazil is the world's second largest producer of beans (Phaseolus vulgaris L.), yet one of the most significant challenges is the prevalence of common bacterial blight (CBB), a disease caused by the bacterium Xanthomonas axonopodis pv. phaseoli. This study examined scientific literature on the genetic improvement of bean plants for resistance to CBB, using data from the Scopus database from 1998 to 2024. The number of publications, geographical distribution, leading authors, and inoculation and improvement strategies were evaluated. Following the refinement of the search parameters, a total of 46 articles were subjected to analysis. Brazil, the United States of America, and Canada were identified as the most prolific countries in terms of the number of publications. Dr. Karl Peter Pauls of Canada and Dr. Rosana Rodrigues of Brazil were particularly prominent on the global academic stage, and consequently, the University of Guelph in Canada and the State University of Norte Fluminense Darcy Ribeiro-UENF in Brazil. The most prevalent inoculation techniques were multi-needle and spraying with a bacterial suspension (1 x 108 cfu.mL-1 ). Gene transfer was primarily conducted through traditional hybridization, with backcrossing being the most prevalent method. An interdisciplinary approach, integrating genetics, molecular biology, and biochemistry, was observed, resulting in the mapping of contributions to bean breeding for resistance to CBB.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".