Differences in the constituents of bacterial microbiota of soils collected from two fields of diverse potato blackleg and soft rot diseases incidences, a case study
Bibliographic record
Abstract
The presence of bacteria from the Dickeya spp. and Pectobacterium spp. in farmlands leads to global crop losses of over $420 million annually. Since 1982, the scientists have started to suspect that the development of disease symptoms in crops might be inhibited by bacteria present in the soil. Here, we characterized in terms of physicochemical properties and the composition of bacterial soil microbiota two fields differing, on the basis of long-term studies, in the occurrence of Dickeya spp.- and Pectobacterium spp.-triggered infections. Majority, i.e. 17 of the investigated physicochemical features of the soils collected from two fields of either low or high potato blackleg and soft rot diseases incidences turned out to be similar, in contrast to the observed 4 deviations in relation to Mg, Mn, organic C and organic substance contents. By performing microbial cultures and molecular diagnostics-based identification, 20 Pectobacterium spp. strains were acquired from the field showing high blackleg and soft rot incidences. In addition, 16S rRNA gene amplicon sequencing followed by bioinformatic analysis revealed differences at various taxonomic levels in the soil bacterial microbiota of the studied fields. We observed that bacteria from the genera Bacillus, Rumeliibacillus, Acidobacterium and Gaiella turned out to be more abundant in the soil samples originating from the field of low comparing to high frequency of pectinolytic bacterial infections. In the herein presented case study, it is shown for the first time that the composition of bacterial soil microbiota varies between two fields differing in the incidences of soft rot and blackleg infections.
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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 | 0.001 | 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) | 0.000 | 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".