Molecular Profiling of Rhizosphere Microbial Communities Associated with Healthy and Diseased Black Spruce ( <i>Picea mariana</i> ) Seedlings Grown in a Nursery
Bibliographic record
Abstract
Bacterial and fungal populations associated with the rhizosphere of healthy black spruce (Picea mariana) seedlings and seedlings with symptoms of root rot were characterized by cloned rRNA gene sequence analysis. Triplicate bacterial and fungal rRNA gene libraries were constructed, and 600 clones were analyzed by amplified ribosomal DNA restriction analysis and grouped into operational taxonomical units (OTUs). A total of 84 different bacterial and 31 different fungal OTUs were obtained and sequenced. Phylogenetic analyses indicated that the different OTUs belonged to a wide range of bacterial and fungal taxa. For both groups, pairwise comparisons revealed that there was greater similarity between replicate libraries from each treatment than between libraries from different treatments. Significant differences between pooled triplicate samples from libraries of genes from healthy seedlings and pooled triplicate samples from libraries of genes from diseased seedlings were also obtained for both bacteria and fungi, clearly indicating that the rhizosphere-associated bacterial and fungal communities of healthy and diseased P. mariana seedlings were different. The communities associated with healthy and diseased seedlings also showed distinct ecological parameters as indicated by the calculated diversity, dominance, and evenness indices. Among the main differences observed at the community level, there was a higher proportion of Acidobacteria, Gammaproteobacteria, and Homobasidiomycetes clones associated with healthy seedlings, while the diseased-seedling rhizosphere harbored a higher proportion of Actinobacteria, Sordariomycetes, and environmental clones. The methodological approach described in this study appears promising for targeting potential rhizosphere-competent biological control agents against root rot diseases occurring in conifer nurseries.
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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 | 0.000 | 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".