Innovative community engagement for the quantitative risk assessment for a mine closure and reclamation plan
Bibliographic record
Abstract
Following the discovery of gold in Yellowknife, Northwest Territories (Canada), Giant Mine officially opened in 1948. Mining activities ceased shortly after the mine’s owner went bankrupt in 1999. Since 2004, the mine has been the responsibility of Crown-Indigenous Relations and Northern Affairs Canada (CIRNAC). Historical activities at the mine have resulted in the generation of arsenic trioxide dust stored in underground chambers, contaminated soil and waste rock, four tailings containment areas, seven open pits, and contaminated water and sediment in Baker Creek, which traverses the mine site. The site has been undergoing progressive reclamation to stabilise the site since 2005, with final closure activities anticipated to be implemented in 2021. The roughly 50-year operating period of the mine resulted in significant disturbance and impacts on the health and lifestyles of local people, especially members of the Yellowknives Dene First Nation (YKDFN) and the North Slave Métis Alliance (NSMA). Giant Mine is within the Akaitcho Dene asserted territory and is close to the YKDFN communities of N’Dilo and Dettah, and is within the traditional land use area of the Tlicho, known as Mowhi Gogha De Niitlee. Giant Mine is also situated within the municipal boundaries of the City of Yellowknife. The closure and reclamation plan for Giant Mine was submitted in April 2019 to the Mackenzie Valley Land and Water Board for approval. The Giant Mine Remediation Project (GMRP) team made decisions about closure options for Giant Mine using input from an extensive engagement process with YKDFN, NSMA, the City of Yellowknife, and other community and government stakeholders. The goals of the GMRP are to minimise public and worker health and safety risks, minimise the release of contaminants from the site into the environment, remediate the site in a way that inspires public trust, and implement an approach that is cost-effective and robust over the long-term. As part of the approval process to commence remediation activities, the project team is required to complete a quantitative risk assessment (QRA). There is an explicit requirement to determine acceptability thresholds in consultation with potentially affected communities and to examine risks from a holistic perspective that includes environmental, social, health, and financial effects. The approval and implementation of the closure and reclamation plan is also occurring within the broader context of reconciliation with Indigenous people in Canada and growing requirements for the federal government to meaningfully engage Indigenous people on actions affecting their lands and resources. Together, these requirements present a unique challenge for the Giant Mine QRA as potentially affected communities rarely participate in, or provide specific input to, a QRA process. An extensive literature review found no publicly available documentation of community involvement in QRAs conducted in natural resource industries. Consequently, the GMRP team and its consultants had to develop an innovative, fit-for-purpose engagement strategy to complete the QRA. This strategy is described herein, and specific outcomes of engagement with Indigenous and stakeholder groups are provided.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".