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Record W3047683525 · doi:10.1201/9781003078692-4

Designing the ventilation system for the McArthur River Mine — World largest uranium deposit

2020· book-chapter· en· W3047683525 on OpenAlexaff
Derek B. Apel

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsCameco (Canada)
Fundersnot available
KeywordsMining engineeringUranium mineUraniumUranium oreGeologyVentilation (architecture)ArchaeologyEnvironmental scienceEngineeringGeographyMetallurgyMechanical engineeringMaterials science

Abstract

fetched live from OpenAlex

Design of safe and efficient ventilation system is always very important aspect of the design process of any underground mine. However, when the mine being designed is the largest uranium mine in the world, the ventilation system becomes the most important aspect of the mine design process. The paper discusses the design process and its implementation of the ventilation system at McArthur River Mine; currently the largest and highest-grade uranium deposit in the world. The paper includes description of the mine ventilation network, mine main fans and heaters arrangements, and also points out many challenges and their solutions to the design and implementation process of the ventilation system at underground uranium

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)0.000
metaresearch head score (Gemma)0.000
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: Other
Teacher disagreement score0.997
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.011
GPT teacher head0.186
Teacher spread0.176 · 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
Published2020
Admission routes1
Has abstractyes

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