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Record W4387261161 · doi:10.36487/acg_repo/2315_036

Evolution of closure planning for an inactive tailings facility

2023· article· en· W4387261161 on OpenAlexaboutno aff
Scott Laberge, Dale Kolstad, Billy Dehler, Art Kalmes

Bibliographic record

VenueMine closure · 2023
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsClosure (psychology)TailingsMaterials scienceMetallurgyPolitical science

Abstract

fetched live from OpenAlex

Sustainable mine closure requires meeting physical, chemical, ecological, and social objectives. Sometimes, these objectives conflict with one another and pose challenges to mine-closure planning. This paper summarizes the key considerations for closure of a tailings facility with emphasis on recent Canadian Dam Association (CDA) and International Council on Mining and Metals (ICMM) guidance. It addresses movement and drying of saturated tailings; closure; final site grading; and water management. The paper also discusses how the approach to closure, landform design, and reclamation of an inactive tailings facility has evolved since initial closure planning began, incorporating institutional knowledge and best practices in dam safety and integrated mine closure. A robust closure plan requires winnowing the options to the most attractive solution and applying a multi-staged approach to closure—one that recognizes environmental stewardship is more than just minimizing potential impacts. A brief case study of an in-progress decommissioning and closure project discusses how these principles are being applied. The case study also introduces the potential for economic benefit and resource gain for the surrounding communities through agricultural or natural-end land uses that will be considered as the design advances. The example demonstrates the benefit of reaching tailings dam sustainability goals that prioritize safety and environmental stewardship.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.493

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 head0.030
GPT teacher head0.249
Teacher spread0.218 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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