Characterization of <i>Ralstonia solanacearum</i> species complex strains causing bacterial wilt of tomato in Louisiana, USA
Bibliographic record
Abstract
A baseline for genetic diversity of Ralstonia solanacearum species complex (RSSC) causing bacterial wilt of field and greenhouse grown Solanum lycopersicum (tomato) in Louisiana was established based on biovar, phylotype and phylogenetic analysis of the egl gene sequence. RSCC strains were characterized as Biovars 1, 3 and 6 and belonged to phylotypes I and II. Phylotypes were subdivided into sequevars based on differences in the nucleotide sequence of the egl gene. Phylotype II strains clustered with closely related North American strains in sequevar 7 and phylotype I strains clustered with Asian strains in sequevar 14. All three RSCC strains isolated from greenhouse grown tomato were phylotype II sequevar 7 and were identical to strain AW1 from tomato in Alabama (AL). While all RSCC strains were pathogenic on tomato ‘Roma’, only six induced a hypersensitive reponse (HR) on Nicotiana tabacum. Four of the HR positive strains were also pathogenic on pepper ‘Yolo Wonder’. This is the first study to characterize RSSC strains causing bacterial wilt of tomato in Louisiana and confirms that strains originating from Asia are present in North America. Genetic characterization of RSCC strains currently present in Louisiana is the first step towards the development of strain-specific management practices, including the identification of strain-specific host resistance.
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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 | 0.001 | 0.001 |
| 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 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".