Restoration of damaged ecosystems in desert steppe open‐pit coal mines: Effects on soil nematode communities and functions
Bibliographic record
Abstract
Abstract Repair of damaged areas in open‐pit (opencast) coal mines has emerged as an important environmental concern. Our research investigated the mechanism by which different ecological restoration methods affected soil nematode communities in damaged areas of desert steppe coal mines. Using high‐throughput sequencing technology, nematode community composition, diversity, and function were analyzed to determine the response of nematodes to different ecological restoration methods in damaged areas of coal mines. For slope rehabilitation, vegetation blanket restoration exhibited more favorable effects than those exhibited by vegetation bag restoration and natural restoration. For rehabilitation of the platform area under the slope, the diversity of soil nematodes and the soil fauna analysis under alien soil restoration conditions were performed and exhibited similar characteristics to those of the native vegetation. Findings based on Linear discriminant analysis Effect Size (LEfSe) multi‐level discriminant analysis and determination of shared genera suggest thatParaphelenchus,Cervidellus,Panagrolaimus,Microdorylaimus,Cephalobus, andEcumenicusmay be the key genera of soil nematodes in the damaged ecosystem of open‐pit coal mines in the desert steppe. We found that reasonable water and fertilizer management in slope restoration and under‐slope platform area restoration may play a key role in the restoration of damaged ecosystems in open‐pit coal mines. We comprehensively analyzed the response of soil nematode communities and their functions to different ecological restoration methods, and provided a reference for evaluating the quality of underground ecosystem restoration of damaged areas in abandoned desert steppe open‐pit coal mines.
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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".