Understanding the relationships between land disturbance, mercury and traditional practices in the Moose Cree Territory: A foundation for risk assessment
Bibliographic record
Abstract
The | Ililiwaskiy (Moose Cree First Nation Territory) in the James Bay region of Ontario, Canada, and like many Indigenous communities across the globe has experienced decades of industrial activity impacting its ecosystems. Community concerns regarding mercury levels in culturally significant fish species led to an interdisciplinary research initiative. This study combined scientific analysis and community knowledge to assess mercury concentrations in three traditionally consumed fish species-walleye (Sander vitreus), northern pike (Esox lucius), and lake sturgeon (Acipenser fulvescens). Researchers engaged with Moose Cree members through workshops, fish sampling training, interviews, and surveys on fish consumption and land/water relationships. Over 180 fish were sampled from six locations. Walleye showed the highest mercury levels, with many samples exceeding Health Canada's guidelines, particularly for walleye over 15 inches., and the 0.2 ppm guideline for subsistence consumers, women, and children. Community interviews underscored the cultural importance of fish and observed environmental changes, as well as changes seen in fish and fishing in the territory. This project revealed not only elevated mercury in traditional food sources but also broader impacts on land-based practices and food security. The findings emphasize the need to rethink water management planning and how contaminant risks from resource extraction are assessed and mitigated. Ultimately, the project supports Moose Cree First Nation leadership in advancing land stewardship and sustaining cultural connections to traditional foods and the land. It also illustrates a collaborative risk assessment process that could be used broadly for bringing together Indigenous knowledge with scientific inquiry to address environmental health concerns.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".