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Record W4415973906 · doi:10.5382/geo-and-mining-29

From Prospect to Postclosure: Essential Tailings Management Knowledge for Exploration Geologists

2025· article· en· W4415973906 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSEG Discovery · 2025
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsVale (Canada)University of Saskatchewan
Fundersnot available
KeywordsTailingsHarmEnvironmental remediationNatural (archaeology)Mineral explorationHazardous wasteRisk managementSurface mining

Abstract

fetched live from OpenAlex

Editor’s note: This is the last paper in the Geology and Mining series, which has aimed to introduce early career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each. The 29 chapters plus two others have been compiled into a book, sponsored by BHP and edited by Dan Wood and Jeffrey Hedenquist, which is now available Open Access on the SEG store (www.segweb.org/store). It will soon be available on GeoScienceWorld, and a limited print run will produce hard copies for purchase. Abstract Mine tailings, the residual materials from mineral extraction, present one of the mining industry’s most complex environmental and engineering challenges. Comprising finely ground rock and residual chemicals, tailings require meticulous management to prevent ecological harm and ensure public safety. For exploration geologists, understanding this is not a downstream consideration but a fundamental responsibility that begins at discovery. The consequences of mismanagement are stark; since 2010, major tailings dam failures have caused numerous fatalities, contaminated thousands of kilometers of waterways, and triggered billions of dollars in remediation costs. These disasters underscore the critical need for specific planning and risk mitigation starting with the exploration phase to prevent similar outcomes. This paper provides exploration geologists with a comprehensive overview of the tailings management life cycle, covering material characterization, surface and underground disposal methods, risk mitigation strategies, best practices in monitoring and closure, and opportunities with tailings reprocessing. It demonstrates that integrating tailings considerations into the earliest phases of exploration—by informing site selection, characterizing geologic materials, and identifying geohazards—offers the most effective and economical path to minimizing long-term liabilities. By embracing their roles as the first stewards of a project, geologists can lay the foundation for safer, more sustainable mining outcomes.

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.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.638

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 head0.010
GPT teacher head0.240
Teacher spread0.230 · 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