The relationships between microbiological attributes and soil and litter quality in pure and mixed stands of native tree species in southeastern Bahia, Brazil
Bibliographic record
Abstract
This study was conducted to link soil and litter microbial biomass and activity with soil and litter quality in the surface layer for different pure and mixed stands of native tree species in southeastern Bahia, Brazil. The purpose of the study was to see how strongly the differences among species and stands affect the microbiological attributes of the soil and to identify how microbial processes can be influenced by soil and litter quality. Soil and litter samples were collected from six pure and mixed stands of six hardwood species (Peltogyne angustifolia, Centrolobium robustum, Arapatiella psilophylla, Sclerolobium chrysophyllum, Cordia trichotoma, Macrolobium latifolium) native to the southeastern region of Bahia, Brazil. In plantations of native tree species in humid tropical regions, the immobilization efficiency of C and N by soil microbial biomass was strongly related to the chemical quality of the litter and to the organic matter quality of the soil. According to the variables analyzed, the mixed stand was similar to the natural forest and dissimilar to the pure stands. Litter microbial biomass represented a greater sink of C and N than soil microbial biomass and is an important contributor of resources to tropical soils having low C and N availability.
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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 | 0.001 |
| 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".