Review on resource and environmental carrying capacity of mining areas in China
Bibliographic record
Abstract
The excessive consumption of resources and the destruction of ecological environment in mining areas have severely restricted coal mining, and the research on resource and environmental carrying capacity (RECC) of mining areas is of great significance in reconciling coal mining and resource environmental protection. To review the existing research on the RECC of mining areas, the concepts of the RECC were elaborated, and then the development process and application areas of both resource carrying capacity (RCC) and environmental carrying capacity (ECC) were studied. The main methods and models involved in the RECC study were counted. The results showed that the research on the RECC of mining areas has not attracted sufficient attention, and the relevant literature is relatively few. The existing research on RECC in mining areas mainly adopts single-factor analysis, which makes it difficult to comprehensively evaluate the overall situation of RECC in mining areas. Thus, a comprehensive evaluation of the RECC of mining areas by scientific evaluation methods is required to ensure the coordinated development of coal production, resources and environment of mining areas. This review provides an important reference and guidance for the necessity and thinking of the comprehensive study of resources and environment carrying capacity in mining 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".