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Record W4387261173 · doi:10.36487/acg_repo/2315_002

The new ISO standard for mine closure and reclamation planning

2023· article· en· W4387261173 on OpenAlexaff
Dirk van Zyl, Michael Nahir, Ian Hutchison

Bibliographic record

VenueMine closure · 2023
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsClosure (psychology)Land reclamationComputer scienceMining engineeringEngineeringPolitical scienceGeographyArchaeology

Abstract

fetched live from OpenAlex

This paper provides an overview of the new ISO standard (ISO 21795) Mine Closure and Reclamation Planning. The Standard contains two separate Parts: Part 1 provides key requirements while Part 2 provides recommendations and guidance. The two overarching objectives of the Standard are to provide a key international resource that encapsulates best practices and related guidance across the many areas of specialty involved with planning for mine closure; and to make this available to a wide range of stakeholders, especially for countries with minimal access to best practices and planning guidance. The intended audience for the Standard includes those with responsibility for, or an interest in, planning for mine closure and reclamation. This includes mine planners and designers, mine operators, regulators, environmental assessors, communities, Indigenous Peoples, and financial stakeholders, amongst others. The Working Group that developed the Mine Closure and Reclamation Planning Standard was made up of over 60 experts representing 13 countries, including several developing countries that can benefit from the best practices and guidance encapsulated by these two documents. The two Parts of ISO 21795 have been prepared to cover the lifecycle of requirements, recommendations, and supporting information that apply to mine closure and reclamation planning, including consideration of mine closure and reclamation objectives, technical procedures, consideration and mitigation of socio-economic impacts, financial planning and assurance, unplanned and post-closure activities, as well as data and knowledge management. This paper describes these requirements, recommendations, and supporting information.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0090.008
Science and technology studies0.0020.002
Scholarly communication0.0080.008
Open science0.0050.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0090.011

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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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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