Comprehensive analysis and risk assessment of tailings storage facilities in China 
Bibliographic record
Abstract
The historical failures of Tailings Storage Facilities (TSFs) in China have led to severe downstream consequences, encompassing loss of life, economic damage, and environmental contamination. Despite these consequences, the comprehensive documentation and quantitative evaluation of TSFs in China have been notably lacking. The existing records of TSFs are incomplete, and there is a deficiency in accurately assessing the frequency of their failures. This gap in knowledge has been a significant obstacle in effectively assessing and mitigating risks associated with TSFs. Our research involved compiling and analyzing new databases, shedding light on the historical failures and current status of TSFs in China. We uncovered 143 TSF failure incidents between 1957 and 2022. This figure largely exceeds the approximately 20 failures reported in earlier studies, highlighting a critical underestimation in past assessments. The human and economic damage of these incidents has been considerable, with 840 lives lost, 1,416 houses damaged, and 28,923 individuals adversely affected. Furthermore, the total volume of tailings released in these failures surpassed 12.7 million m3. A notable observation from our study is that about 75% of these failures involved tailings flowing into water bodies, exacerbating environmental pollution significantly. Our study also presents an in-depth statistical analysis of the magnitude and frequency of these failures. We found that the average return period for TSF failures in China, resulting in at least 10 fatalities, is approximately every five years. For failures with released volumes exceeding 1 million m3, the average return period extends to about 16 years. In addition to historical data, we include a comprehensive review of current TSFs. Our review confirms that there are 14,217 existing TSFs in China alone, leading to an estimated cumulative failure rate of approximately 1%. Our work further includes the development of a supplementary database encompassing 1,853 TSFs, providing essential statistics such as storage volume and dam height. This database is a crucial tool for ongoing and future risk assessments. Applying our database-driven, regionally-simplified risk assessment approach, we conducted a case study in Jilin Province. The results are concerning, indicating 11 TSFs bearing intolerable risks, among which the most hazardous TSF presents a potential loss of life estimated at 175 individuals. Our study offers the most comprehensive overview of TSF failures and their implications in China to date. The extensive scope of this research bears substantial implications for prospective nationwide utilization, particularly in the enhancement of risk assessment methodologies and the enforcement of efficacious mitigation measures for TSFs in China.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".