Exposure to Arsenic in Yellowknife, Northwest Territories, Canada
Bibliographic record
Abstract
Giant Mine, located in Yellowknife, Northwest Territories, is regarded as one of the most contaminated sites in Canada. Gold extraction from arsenopyrite ores left behind a legacy of 237,000 tonnes of arsenic trioxide stored in the inactive mine’s underground chambers. Contamination of such scale is of public health concern due to the potential hazard of arsenic and other chemical exposures to the Yellowknife population as well as the local Indigenous communities: Yellowknives Dene First Nation and members of the North Slave Métis Alliance. The objective of this study is to investigate arsenic exposure in the human population of Yellowknife using a biomonitoring approach with a longitudinal cohort design. We postulate that exposure levels in the Yellowknife population are similar to that that of the Canadian population, resulting in no observable effects. A community-participatory approach is used for the development of the study design. Consultations with local stakeholders started in 2016 leading to the establishment of a Advisory Committee that oversees all aspects of the study. Baseline data are to be collected from 10% of Yellowknife households through random selection, representative of the population, and volunteer participants from the Indigenous communities from 2017-2018. Urine, toenail and saliva samples are being collected from about 2000 participants ranging from 3 to 79 years old. Total and arsenic species concentrations in urine and toenails are measured using LC-ICP-MS, and compared to the data reported by the Canadian Health Measures Survey. Saliva samples are collected for the analysis of 89 single nucleotide polymorphisms of genes related to arsenic metabolism. Two protein biomarkers CC16 and KIM-1 in urine samples will be measured as biomarkers for kidney and lung functions. Our research is the first comprehensive biomonitoring and health survey conducted in the area of this major contaminated site in Canada.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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 teacher head, 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".