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Record W3212065795 · doi:10.3390/min11111234

A Failure Modes and Effects Analysis Framework for Assessing Geotechnical Risks of Tailings Dam Closure

2021· article· en· W3212065795 on OpenAlexafffundabout
Haley L. Schafer, Nicholas Beier, Renato Macciotta

Bibliographic record

VenueMinerals · 2021
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of Alberta
FundersCanadian Natural Resources Limited
KeywordsClosure (psychology)TailingsTailings damDam failureEngineeringEnvironmental planningRisk analysis (engineering)Environmental scienceCivil engineeringBusiness

Abstract

fetched live from OpenAlex

Tailings dams remain on site following mine closures and must be designed and reclaimed to meet long-term goals, which may include walk-away closure or long-term care and maintenance. The underperformance of these structures can result in significant risks to public and environmental safety, as well as impacts on the future land use and economic activities near the structure. In Alberta, Canada, the expectation is for a tailings dam to be reclaimed and closed so that it can undergo deregistration. To aid in assessing the risks of underperformance during and after closure, a Generalized Failure Modes and Effects Analysis (G-FMEA) framework was developed to assess the long-term geotechnical risks for tailings dams in Alberta, with the goal of assessing the potential success of a tailings dam closure strategy. The G-FMEA is part of an initiative to enhance closure evaluations in Alberta in a collaborative effort between industry, the regulator, and academia. The G-FMEA incorporates the element of time to account for the evolution of the system, which should be applied at the planning stage and updated continually throughout the life of the facility. This paper presents the developed G-FMEA framework for tailings dams in Alberta, including the developed risk matrix framework.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.291
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2021
Admission routes3
Has abstractyes

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