Effects of reclamation years on composition and diversity of soil bacterial communities in Northwest China
Bibliographic record
Abstract
The objective of this study was to evaluate bacterial community structure and diversity in soil aggregate fractions when salinized farmland was reclaimed after >27 years of abandonment and then farmed again for 1, 5, 10, and 15 years. Illumina MiSeq high-throughput sequencing was performed to characterize the soil bacterial communities in 5 aggregate size classes in each treatment. The results indicated that reclamation significantly increased macro-aggregation (>0.25 mm), as well as soil organic C, available N, and available P. The 10-year field had the largest proportion (93.9%) of soil in the macro-aggregate size classes (i.e., >0.25 mm) and the highest soil electrical conductivity. The 5 most dominant phyla in the soil samples were Proteobacteria, Actinobacteria, Gemmatimonadetes, Acidobacteria, and Bacteroidetes. The phylogenetic diversity, Chao1, and Shannon indices increased after the abandoned land was reclaimed for farming, reaching maximums in the 15-year field. Among aggregate size classes, the 1-0.25 mm aggregates generally had the highest phylogenetic diversity, Chao1, and Shannon indices. Soil organic C and soil electrical conductivity were the main environmental factors affecting the soil bacterial communities. The composition and structure of the bacterial communities also varied significantly depending on soil aggregate size and time since reclamation.
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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 | 0.001 | 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".