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Record W1556545254

Giant Mine: Historical Summary

2012· article· en· W1556545254 on OpenAlexafffundabout
Arn Keeling, John Sandlos

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsArsenicArsenic poisoningTailingsMining engineeringPollutionEnvironmental scienceWaste managementGeologyEngineeringMetallurgy
DOInot available

Abstract

fetched live from OpenAlex

This report examines the history of arsenic pollution from Giant Yellowknife Gold Mine, and its environmental and public health impacts. It is based on extensive archival research in national and territorial archives, as well as oral histories (published and unpublished) and a review of public reports on the issue. Operating from 1948 to 2004, Giant Mine produced over 7 million ounces of gold mined from arsenopyrite ore formations located on the north shore of Yellowknife Bay. Gold processing entailed roasting the ore, producing as a byproduct arsenic trioxide dust, a highly toxic form of arsenic. In the early 1950s, arsenic emissions from Giant and Con mines totalled an estimated 22,000 lbs. per day. Though Con mine installed a scrubber in 1949, Giant mine (the source of most of the arsenic) did not install pollution control equipment until the end of 1951, after a Dene child died of acute arsenic poisoning at Latham Island. Local livestock also died as from arsenic poisoning. Government and mine officials met at the time to discuss how to address the problem of arsenic pollution, but never contemplated even a temporary shutdown of the mine. The capture of arsenic using a Cottrell Electrostatic Precipitator reduced but did not eliminate arsenic. Collection rates were further improved by the installation of a baghouse in 1958. Studies undertaken through the 1950s and 1960s revealed persistent high levels of arsenic on local produce and berries. Concerns remained about water pollution from both atmospheric deposition and arsenic- and cyanidelaced tailings effluent. The collection of arsenic, , also resulted in the fateful decision to store arsenic trioxide dust in underground chambers and mined-out stopes. Today, the 237,000 tonnes of arsenic trioxide underground at Giant remains the central environmental challenge for the reclamation and remediation of the site. Public health studies undertaken in the 1960s suggested a possible link between arsenic exposure and elevated cancer rates in Yellowknife, but these studies were not made public until the 1970s. A series of independent and government studies followed these revelations as public concern mounted over the health effects of long-term arsenic exposure. Further reductions in arsenic emissions from Giant were achieved, and the mine constructed a tailings effluent treatment system in 1981. While local activists raised concerns about sulphur dioxide and arsenic emissions in the early 1990s, territorial government studies concluded these emissions did not pose a public health risk. For the Yellowknives Dene First Nation in particular, memories of mine development and subsequent arsenic pollution of their traditional lands are painful. Some Dene worked at the mine, but the communities of Ndilo and Dettah saw few benefits from the mines overall. Yet these communities, due to their location in relation to Giant, were on the front line of arsenic exposure over the half-century ofits operation. The Yellowknives Dene not only suffered disproportional health impacts from arsenic pollution, but also the loss of harvesting areas due to the appropriation of land for the mining operations and urban growth in the city of Yellowknife.

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)0.001
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0030.002
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0540.022

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 head0.049
GPT teacher head0.295
Teacher spread0.246 · 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
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

Citations21
Published2012
Admission routes3
Has abstractyes

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