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Record W3201231868 · doi:10.28927/sr.2021.067521

The new expertise required for designing safe tailings storage facilities

2021· article· en· W3201231868 on OpenAlexaboutno aff
G WILSON

Bibliographic record

VenueSoils and Rocks · 2021
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsTailingsDrainageMining engineeringMountEnvironmental scienceTailings damEngineeringEcology

Abstract

fetched live from OpenAlex

The global mining community has seen a dangerous sequence of failures in tailings dams, beginning with Mount Polley mine, followed by the Samarco, Cadia Valley and Córrego do Feijão mines. This sequence of failures began on August 4, 2014, at the Mount Polley tailings storage facility in British Columbia, Canada. The initial failure in the embankment at the Mount Polley tailings storage facility had substantial impact on the global mining industry. The Independent Expert Engineering Investigation and Review Panel (IEEIRP) tasked with the investigation of the breach in the tailings dam at Mount Polley made major contributions for new guidelines. The incident has given rise to comprehensive recommendations for best available tailings technologies (BAT) based on principles such as the elimination of surface water from impoundments with the promotion of unsaturated conditions in the tailings through drainage provisions. The application of these BAT principles for the surface storage of tailings leads to the use of filtered tailings technology. Filtered tailings technology or “dry stack tailings” can satisfy each of the BAT components when the impoundment is properly designed and constructed. The implementation of the best available technologies for the physical stability (BAT-PS) of tailings impoundments competes directly with the best available technologies for the chemical stability (BAT-CS) of reactive tailings that may produce acid and metalliferous drainage. The new expertise in mine waste management required to achieve both BAT-PS and BAT-CS are discussed in the present paper.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score0.305

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 head0.019
GPT teacher head0.210
Teacher spread0.192 · 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 designNot applicable
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

Citations4
Published2021
Admission routes1
Has abstractyes

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