Species Diversity of <i>Dickeya</i> and <i>Pectobacterium</i> Causing Potato Blackleg Disease in Pakistan
Bibliographic record
Abstract
Potato blackleg is caused by a diverse species of pectinolytic bacteria. In Pakistan, approximately 90% of the pathogens involved belong to Pectobacterium atrosepticum. Survey (2014 to 2017), sampling, and isolation from different potato growing areas of Punjab, Pakistan depicted an overall disease incidence of approximately 15%. Thirty-six pectinolytic strains confirmed through biochemical and pathogenicity testing were characterized via gapA gene to identify them at the species level. To further validate the identification, one strain from each species SS26 (P. atrosepticum), SS28 (Pectobacterium polaris), SS70 (Dickeya dianthicola), SS90 (Pectobacterium parmentieri), SS95 (Pectobacterium punjabense), and SS96 (Pectobacterium versatile) were selected for draft genome sequencing and multilocus sequence analysis of 13 housekeeping genes (fusA, rpoD, acnA, purA, gyrB, recA, mdh, mtlD, groEL, secY, glyA, gapA, and rplB). Phylogenetic analysis revealed considerable genetic diversity in the genus Pectobacterium. In silico DNA–DNA hybridization and average nucleotide identity values of the strains selected for genome sequencing were determined with other reference Pectobacterium and Dickeya strains. Moreover, all six representative strains were also phenotypically characterized on the basis of metabolism of different carbon sources. Overall, on the basis of genotypic and phenotypic characteristics, these 36 isolates were grouped into six species: P. atrosepticum, P. versatile, P. parmentieri, P. polaris, P. punjabense, and D. dianthicola.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".