Changes in rhizosphere bacterial communities associated with tree decline: grapevine esca syndrome case study
Bibliographic record
Abstract
An investigation was carried out on rhizosphere bacteria to determine if they may be associated with perennial crops affected by nonspecific decline, a phenomenon that is difficult to diagnose and prevent. Esca disease of grapevine was chosen for this case study because of its easy foliar symptom identification. Ribosomal DNA fingerprint analysis by polymerase chain reaction – denaturing gradient gel electrophoresis (PCR–DGGE), quantitative PCR (qPCR), and rDNA amplicon sequencing by next-generation sequencing (NGS) were adopted to investigate the bacterial communities associated with grapevines, which were selected for the presence and absence of external foliar symptoms in 11 vineyards. According to PCR–DGGE and qPCR, bacterial communities differed in site of origin (vineyards), but not between symptomatic and asymptomatic plants, whereas qPCR gave a significantly higher presence of total bacteria and Pseudomonas spp. in asymptomatic plants. NGS confirmed no difference between symptomatic and asymptomatic plants, apart from a few minor genera (<0.5%) such as Salinibacterium, Flavobacterium, Nocardia, and Janthinobacterium, which were, in all cases, higher in asymptomatic plants and whose functional role should be the object of further investigation. The fact that total bacteria and Pseudomonas were more abundant in the rhizosphere of asymptomatic grapevines and that some bacterial genera were associated with the latter, represents a new element when investigating the multiple-origin phenomenon such as esca disease of grapevine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".