Microbial Community Composition Associated with Potato Plants Displaying Early Dying Syndrome
Bibliographic record
Abstract
Potato early dying disease complex (PED) leads to premature senescence and rapid decline in potato plants. Unlike potato wilt caused solely by Verticillium species, PED symptoms are more severe due to the synergistic effects of multiple pathogens, including root-lesion nematodes, fungi such as Colletotrichum and Fusarium, and soft-rot bacteria. To investigate the microbiome responsible for PED, soil and stem samples from healthy-looking and symptomatic plants were analyzed using amplicon-targeted next-generation sequencing (Illumina MiSeq and PacBio technologies). Samples were collected from four locations in New Brunswick, Canada from fields previously rotated with barley or oat. Comparative analysis of the bacterial, fungal, and eukaryotic diversity in soil samples showed minimal differences, with only bacterial alpha diversity influenced by the plant health status. Verticillium dahliae was abundant in all soil samples, and its abundance was significantly higher in the stems of diseased plants. Additional fungal species implicated in PED, including Plectosphaerella cucumerina, Colletotrichum coccodes, Botrytis sp., and Alternaria alternata, were also identified in the stems. This study highlights the complex, plant-associated microbial interactions underlying PED and provides a foundation for microbiome-informed disease management strategies.
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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 | 0.000 |
| 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".