A Quantitative Scenario Analysis Method for Ecological Safety Development Trends in Rare Earth Mining Areas
Bibliographic record
Abstract
Rare earth mining can cause ecological problems as a result of soil erosion and water pollution in local areas.Thus assessing the development trends of ecological safety is crucial in formulating the environmental protection policies in rare earth area.Rare earth mining is a long and complicated process in which the state values of evaluation indicators for ecological safety are dynamic and continual interplay.Hence, conventionally measured indicators are inadequate in this context.An evaluation method based on the theory of quantitative scenario analysis was proposed in this study to analyze ecological safety development trends in rare earth mining areas.First, according to the PSR model framework, six indicators including "mining technology", "mining intensity", "water environment", "soil environment", "waste water, waste gas and waste residue management technologies" and "environmental protection policy", were selected to reveal the ecological safety in mining areas.Second, the crossover probability algorithm, Markov chain and nonlinear programming were utilized to construct a quantitative scenario analysis model for ecological safety development trends.Lastly, the model was verified using the data on Lingbei rare earth mining area located in Ganzhou City, China.Results showed that the quantitative scenario analysis model could be used to calculate the changes in various indicators, their cross impacts in the development process and the occurrence probability of scenario combinations for ecological safety development that were composed of the state changes of each indicator.These findings indicate that the proposed model can effectively and accurately forecast the ecological safety development trends in rare earth mining areas.The conclusions can provide a theoretical basis for environmental protection work in rare earth 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".