Effect of Fungicide Application on Lowbush Blueberries Soil Microbiome
Bibliographic record
Abstract
Lowbush blueberries (Vaccinium sp.) are perennial crops produced throughout eastern Canada and Maine through management of wild populations. Given the constraints of this cropping system, the application of fungicides is critical to reducing disease pressure and ensuring consistent yields. However, as plant health is intertwined with soil health, it is important to consider the impact of fungicides on microbial communities. To understand the effects of fungicides in this context, bacterial and fungal microbial communities from fungicide-treated plots, as well as untreated control plots (UTG) were analyzed using amplicon sequencing. The fungicides, considered collectively as a combined treatment group (CTG), lead to a loss in fungal richness. One family, Clavariaceae, had an increased abundance under prothioconazole relative to UTG. This finding may be significant as taxa in Clavariaceae have been thought to potentially form ericoid mycorrhizae with Vaccinium. Five functional pathways and 74 enzymes differed significantly in relative abundance between CTG and UTG including enzymes associated with soil nutrient cycles. Most notably, enzymes corresponding to the breakdown of halogen-organic compounds had an increased abundance in CTG, suggesting bacterial fungicide degradation. Some enzymes associated with soil nutrient cycles differed significantly, possibly implying changes to nutrient pathways due to fungicide treatment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".