Structural and functional differentiation of bacterial communities in post-coal mining reclamation soils of South Africa: bioindicators of soil ecosystem restoration
Bibliographic record
Abstract
Soil microbial communities are suitable soil ecosystem health indicators due to their sensitivity to management practices and role in soil ecosystem processes. Presently, information on structural and functional differentiation of bacterial communities in post-coal mining reclamation soils of South Africa is sparse. Here, bacterial communities in three post-coal mining reclamation soils were investigated using community-level physiological profiling (CLPP), enzyme activities, and next-generation sequencing of 16S rRNA gene. Inferences were drawn in reference to adjacent unmined soils. CLPP-based species diversity and proportionality did not differ significantly (P > 0.05) whereas activities of β-glucosidase, urease and phosphatases were significantly (P < 0.05) influenced by site and soil history (reclaimed vs unmined). Bacterial communities were influenced (PERMANOVA, P < 0.05) by soil history and site differences, with several phylotypes differentially abundant between soils. Contrastingly, predicted functional capabilities of bacterial communities were not different (PERMANOVA, P > 0.05), suggesting redundancy in bacterial community functions between reclamation and unmined soils. Silt content, bulk density, pH, electrical conductivity, Na and Ca significantly influenced soil bacterial communities. Overall, results indicate that bacterial community structure reflects underlying differences between soil ecosystems, and suggest the restoration of bacterial diversity and functions over chronological age in reclamation soils.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".