Risk assessment and management for an extreme accident at a waste slag site
Bibliographic record
Abstract
Abstract. Waste slag failure is a disaster that affects both the environment and people. In China there are many waste slags after the engineering project such as railway and road. Although the disposal of the waste slags obeys the regulations the government have established, some accident still happened. Therefore, we need to do inverse analysis to check the failure extend and impact area of each slope. The probability of failure of a waste slag site involves many unpredictable factors that are hard to calculate. Therefore, we propose a risk analysis and management scheme for extreme accidents that assumes failure will arise at extreme conditions, and emphasize risk management in the design and monitoring of the slag site. In this scheme, we use Tsunami–Square Method to simulate the flow of tailings sand to get the intensity parameters (flow path and thickness) to create hazard zones based on dam failure. The risks to buildings and people were analysed according to the vulnerability of the buildings in the flow path. A risk sharing community risk management mode is presented using the idea of Canadian Whitehorse Mining Initiative, which sparkplug multi-stakeholder representatives to participate in risk management. Following the As Low As Reasonably Possible principle, the risk management scheme divides areas at risk into five zones in the F–N Curve. These zones have different mitigation measures for risk from tailings ponds and other waste slag sites. The scheme is effective for determining design safety factors, implementing reinforcements, and monitoring the waste slag site, and encouraging multiparty participation in risk management.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".