Variation in Bacterial and Eukaryotic Communities Associated with Natural and Managed Wild Blueberry Habitats
Bibliographic record
Abstract
We examined the differences between bacterial and eukaryotic soil communities associated with natural and managed habitats of wild blueberry, Vaccinium angustifolium. In total, 138 bacterial and 130 eukaryotic soil and rhizosphere communities across seven blueberry fields, all established at least 30 years ago and from two forest areas adjacent to some of these fields, were analyzed. We analyzed correlations between soil chemical factors and the structure of eukaryotic and bacterial communities, including differences in the microbiome between bulk and rhizosphere soils, and between rhizospheres of plants growing in natural and managed habitats. Characterization of a broad selection of fields across the province of Nova Scotia, Canada, allowed us to tentatively identify specific signatures from several distinct soil niches. Our data indicate that bacterial and eukaryotic communities differ in how they correlate with soil chemical properties. Also, while eukaryotic communities correlate stronger with soil fertility than bacterial communities, plant selection had a stronger effect on bacterial microbiomes than on eukaryote microbiomes. Additionally, we found that the composition of root-associated bacterial communities differs between managed and natural wild blueberry habitats, confirming previous reports that management can affect rhizosphere microbiomes.
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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 | 0.000 | 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".