Change of soil microorganism communities under saline‐sodic land degradation on the Songnen Plain in northeast China<sup>#</sup>
Bibliographic record
Abstract
Abstract Background The Songnen Plain in northeast China was one of three major grasslands in China and has now become one of the most concentrated areas of sodic‐saline soil worldwide due to soil parent material characteristics, hydrological conditions, and overgrazing. Aims The aim of this study was to evaluate the direct effects of environmental factors in shaping prokaryotic communities under three natural contiguous areas (severe saline‐alkaline field with no vegetation, moderate saline‐alkaline field withSuaeda glauca, and mildly saline‐alkaline field with natural grass vegetation) on the Songnen Plain in northeast China. Methods Physicochemical properties of the soil pH, electric conductivity (EC), and soil organic carbon (SOC) were determined in three soil types with or without vegetation, while the metabarcoding analysis of the prokaryotic diversity and composition were analyzed by Illumina Miseq sequencing. Results Our study revealed that the moderate and mildly saline‐alkaline soil exhibited lower pH by 0.614% and 10.17%, and significantly lower EC by 47.96% and 89.22%, respectively, in comparison to severe saline‐alkaline field soil. Prokaryotic 16S rRNA gene amplicon analysis revealed that mildly saline‐alkaline soil with native grasses had significantly higher alpha‐diversity. The composition of the prokaryotic community was highly correlated with the soil physicochemical properties, but the SOC was the most important driving forces for the prokaryotic composition. Random matrix theory (RMT) network analysis revealed the keystone operational taxonomic unit (OTU) (OTU3096, OTU137, OTU3198 and OTU2210) that was significantly affected by the soil physicochemical properties in the three contiguous areas. Conclusions Collectively, these findings demonstrate that the mildly saline‐alkaline soil with natural grass vegetation has a beneficial impact on the soil physical properties and prokaryotic community relative to severe saline areas in the Songnen Plain of northeast China.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".