Influence of herbaceous litter thickness on bacterial community structure and physicochemical properties of aeolian sand
Bibliographic record
Abstract
The change and mechanism of soil and soil bacterial diversity during the change of herbaceous litter thickness in desert areas is crucial to understand. In the study, the dominant herbaceous litter mixture in Baijitan National Nature Reserve was selected as the research material, and an experiment was established by adjusting the litter depth. The results showed that the measured values of soil physicochemical factors (total nitrogen, total protein, total potassium, available phosphorus, available potassium, pH, and soil water content) increased with the increase of herbaceous litter mixture thickness in 0–5 cm soil layer. Actinobacteria, Proteobacteria, Acidobacteria, Chloroflexi, and Gemmatimonadetes were the dominant bacterial phyla under different thicknesses of herbaceous litter in 0–5 and 5–10 cm soil layers. Balneimonas, Rubrobacter, and Geodermatophilus were the dominant bacterial genera under different thicknesses of herbaceous litter in 0–5 and 5–10 cm soil layers. There was no obvious change in the α-diversity index of bacterial community the same soil layer, but the α-diversity index in the 0–5 cm soil layer was lower compared to the 5–10 cm soil layer. The results of this study revealed that the change of herbaceous litter thickness had no significant effect on soil bacterial community structure in desert areas.
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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 | 0.000 |
| 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".