Depth-dependent influence of different land-use systems on bacterial biogeography
Bibliographic record
Abstract
Despite progress in understanding microbial biogeography of surface soils, few studies have investigated depth-dependent distributions of terrestrial microorganisms in subsoils. We leveraged high-throughput sequencing of 16S rRNA genes obtained from soils collected from the RARE: Charitable Research Reserve (Cambridge, ON, Canada) to assess the influence of depth on bacterial communities across various land-use types. Although bacterial communities were strongly influenced by depth across all sites, the magnitude of this influence was variable and demonstrated that land-use attributes also played a significant role in shaping soil bacterial communities. Soil pH exhibited a large gradient across samples and strongly influenced shifts in bacterial communities with depth and across different land-use systems, especially considering that physicochemical conditions showed generally consistent trends with depth. We observed significant (p ≤ 0.001) and strongly correlated taxa with depth and pH, with a strong predominance of positively depth-correlated OTUs without cultured representatives. These findings highlight the importance of depth in soil biogeographical surveys and that subsurface soils harbour understudied bacterial members with potentially unique and important functions in deeper soil horizons that remain to be characterized.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".