Reclamation of desert land to different land‐use types changes soil bacterial community composition in a desert‐oasis ecotone
Bibliographic record
Abstract
Abstract Understanding the impacts of different land‐uses on soil microbial communities is essential for maintaining soil health and sustainability in a desert‐oasis ecotone. Information on the microbial community composition of reclaimed soils under different land‐use types after several decades of reclamation are limited. The objective of this study was to investigate the impacts of reclamation of the non‐productive desert to productive lands on soil microbial community composition and identify the critical soil chemical factors associated with these changes. Soil samples were collected from a control (natural desert land [DL]) and reclaimed lands: cotton land (CL), grape land (vinyards) (GL), and shelterbelt (SL). Soil microbial community composition and diversity were determined by high throughput sequencing. The results showed that soil organic carbon (SOC), total nitrogen (N), phosphorus (P), potassium (K), and pH were significantly different between DL and reclaimed soils (CL, GL, and SL). Sixty years after reclamation, the CL contained a higher relative abundance of Actinobacteria, while the GL and SL contained a higher relative abundance of Acidobacteria. There were 541 operational taxonomic units (OTUs) shared by all the four land‐use types. The highest number of shared OTUs was found in the GL and SL. The variance observed in the bacterial communities in all land‐use types were mainly explained by SOC, followed by total N, total K, pH, and total P. Our results suggest that land‐use type change has significant impacts on soil bacterial community composition and diversity through modifications in soil chemical properties in desert‐oasis ecotone.
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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 | 0.001 |
| 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".