Mercury concentrations in historic autopsies from Grassy Narrows First Nation
Bibliographic record
Abstract
The Asubpeeschoseewagong Anishinabek (Grassy Narrows First Nation) have been engaged in a decades-long struggle to improve their health and environment after an industrial discharge of between 9000 and 11,000 kg of mercury (Hg) into their river system. Hg concentrations in freshwater fish, central to their cultural identity, livelihood and diet, were among the highest ever reported. Between 1972 and 1992, a Canadian government program measured Hg concentrations in routine autopsies from this community. In 2017, Grassy Narrows obtained their community's autopsy reports. The present study examined the distribution of total mercury (T-Hg) and inorganic mercury (I-Hg) in brain, organ, blood, and hair samples from 21 historic autopsy reports, spanning 1976 to 1986. T-Hg median in blood and hair were 6 ppb (range = 2.5-100) and 2.47 ppm (0.41-49.8), respectively. Hg was present in all brain regions (T-Hg median = 53 ppb, 13-299), with highest concentrations in the cerebellum (63 ppb, 16-365) and basal ganglia (58 ppb, 10-420). I-Hg constituted approximately 25 % of T-Hg in all brain regions. In organ samples, T-Hg was higher [renal medulla (290 ppb, 28-4400), renal cortex (1240 ppb, 100-6000), liver (300 ppb, 64-2400)], with greater proportion of I-Hg (82 %, 74 %, 63 %, respectively). Significant correlations were observed between T-Hg in hair and most brain regions (ρ = 0.70-0.77), blood (ρ = 0.56), and renal cortex (ρ = 0.61). While Hg accumulation in the cerebellum has been documented, the basal ganglia has seldom been an object of interest in the Hg scientific literature. The presence of Hg in the brain and other organs complement current studies on the long-term health consequences of Hg in this community. The findings further suggest the need for a closer examination of the role of basal ganglia in Hg-related disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".