Understanding the Capability of an Ecosystem Nature-Restoration in Coal Mined Area
Bibliographic record
Abstract
Ecosystem issues have been severely concerned and studied when the coal resource is one of major energy generators, and green mining innovation techniques involving artificial-restorations have addressed and significantly lessened negative impacts on the ecological environment. The ecosystem of a coal-mined area, however, is able to naturally restore with the processes of natural succession, similar to the human body system that has the immune ability to self-heal a wound over time if the wound does not deeply hurt the health. Here we analyze multiple discipline real data from two mining sites, and evidently show an ability of nature that the coal mining related problems such as geological cracks, damaged aquifers and destroyed soils in Quaternary period can naturally recover around a half-year after the end of mining. Our results temporally and spatially demonstrate that the damaged ecosystem has a capability of unaided nature-remediation from the ground to the subsurface, which is very useful to the countries worldwide with abundant coal reserves and intense energy demands for their development.
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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 | 0.001 |
| 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".