Determinants of bacterial communities in <scp>C</scp> anadian agroforestry systems
Bibliographic record
Abstract
Land-use change is one of the most important factors influencing soil microbial communities, which play a pivotal role in most biogeochemical and ecological processes. Using agroforestry systems as a model, this study examined the effects of land uses and edaphic properties on bacterial communities in three agroforestry types covering a 270 km soil-climate gradient in Alberta, Canada. Our results demonstrate that land-use patterns exert stronger effects on soil bacterial communities than soil zones in these agroforestry systems. Plots with trees in agroforestry systems promoted greater bacterial abundance and to some extent species richness, which was associated with more nutrient-rich soil resources. While Acidobacteria, Actinobacteria and Alphaproteobacteria were the dominant bacterial phyla and subphyla across land uses, Arthrobacter, Acidobacteria_Gp16, Burkholderia, Rhodanobacter and Rhizobium were the keystone taxa in these agroforestry systems. Soil pH and carbon contents emerged as the major determinants of bacterial community characteristics. We found non-random co-occurrence and modular patterns of soil bacterial communities, and these patterns were controlled by edaphic factors and not their taxonomy. Overall, this study highlights the drivers and co-occurrence patterns of soil microbial communities in agroforestry systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".