Bibliographic record
Abstract
Tailings storage facilities (TSFs) and conventional water retaining dams are the largest manmade structures on Earth. Statistics show that TSFs are more likely to fail than water retaining dams.Recent catastrophic failures of TSFs have led to the loss of lives (Germano mine, Brazil), environmental damage (Mount Polley, Canada), contamination of drinking water (Baia Mare, Romania), and the destruction of property (Kingston Fossil Plant, USA). As the scale of mining increases, TSFs increase in height and volume, therefore increasing the consequence of failure. To help mitigate risk associated with large TSFs mining companies empanel expert groups to review operations of TSFs and conduct regular visual inspections. In the US the Mine Safety and Health Administration has regulatory responsibility for the safety of TSFs. As population centers expand nearer to existing and proposed TSFs, the public requires assurance of the integrity of these structures. A pro-active approach to public safety is more desirable than a post-mortem analysis after a major failure. We have examined both the regulatory practices, the industry practices, and public data on TSFs in Arizona. In this thesis paper we address inadequacies of the official government records on TSFs in the two largest publicly accessible databases of dams inthe US – the National Inventory of Dams (NID), and the National Performance of Dams Program (NPDP). Both databases contain numerous errors and omissions, including descriptions and geographic coordinates of TSFs that are inaccurate by many kilometers. Several large TSFs in Arizona are not included in either database.We address these shortcomings with a pilot project for Arizona that demonstrates recording accurate information in a database is neither expensive nor onerous, communicating best practices for operation can help alleviate community concerns, and continuous monitoring technology can resolve shortcomings with visual inspections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".