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Record W4409359810 · doi:10.1139/as-2024-0083

An arctic case study of revegetation planning to implementation at Snap Lake Diamond Mine

2025· article· en· W4409359810 on OpenAlexaffvenueabout
Melissa Amanda Hope Turcotte, Jamie Van Gulck, Drew Stavinga, Michelle Peters

Bibliographic record

VenueArctic Science · 2025
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsDe Beers (Canada)Queen's University
Fundersnot available
KeywordsRevegetationArcticSnapMining engineeringDiamondThe arcticGeologyOceanographyLand reclamationArchaeologyGeographyComputer science

Abstract

fetched live from OpenAlex

Closure planning is a requirement of the permitting and licencing process for mines in Canada and this typically includes revegetation of land disturbed by the development and operations of the mine. At the Snap Lake Mine located in the Northwest Territories of Canada, revegetation planning and design spanned over 20 years, originating to support initial permitting through to the final regulatory approvals of the mine's revegetation design and implementation at site. The purpose of this paper is to present a case study for the revegetation design completed for the Snap Lake Mine. This case study examines the influence of the permitting and regulatory process, engagement with stakeholders, and the outcomes of scientific and technical research on the revegetation design for a remote, cold region mine. It was demonstrated that consideration of regulatory requirements and stakeholder wants is just as important as the technical and scientific findings when developing a revegetation design for a mine. Key learnings presented in this case study can be used to inform researchers and decision-makers of the planning and design requirements for revegetation of northern mines, including the legal, social, and technical aspects that influence revegetation planning and the overall timeline for the revegetation design process.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
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 head0.066
GPT teacher head0.493
Teacher spread0.426 · 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.

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
Published2025
Admission routes3
Has abstractyes

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