Changes in soil macrofaunal community composition under selective afforestation in shifting sand lands in Horqin of Inner Mongolia, northern China
Bibliographic record
Abstract
Abstract Large‐scale afforestation programs have had some beneficial effects on reducing severity of dust storms and controlling desertification in arid and semi‐arid regions. However, the influences of selective afforestation on soil arthropod community are largely unknown in desertified ecosystems. Soil macrofaunal communities, soil physico‐chemical properties, and herb vegetation were investigated in afforested shrublands and woodlands (both approximately 30 years old post‐afforestation), which were compared to shifting sand lands in Horqin, northern China. In the shrublands, environmental parameters (soil and vegetation properties) indicated a significant improvement of soil organic carbon, total nitrogen, and herbaceous density and cover, in comparison to the woodlands and shifting sand lands. The improved shrubland habitat maintained significantly higher soil macrofaunal abundance and group richness together with higher diversity compared with the woodlands and shifting sand lands. There were no significant differences in soil macrofaunal diversity between the woodlands and shifting sand lands. The results suggest that shrubs can facilitate macrofaunal assemblies and improve soil and vegetation properties when planted in shifting sand lands. Shrub afforestation is beneficial for the restoration of shifting sand lands, and is recommended for management of artificial plantations in these sandy ecosystems.
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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 | 0.000 |
| 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".