Mercury and selenium in Beluga teeth: tools for biomonitoring and dietary exposure assessment
Bibliographic record
Abstract
Beluga teeth are evaluated as biomonitors of heavy metal accumulation in beluga soft tissues and contaminant exposure in people who consume beluga as part of a traditional diet.Selenium, which protects marine mammals from the toxic effects of mercury, was measured in beluga teeth for the first time using hydridegeneration atomic fluorescence spectrometry.Tooth selenium concentrations are shown to be moderately strong predictors of liver and muscle selenium, validating the use of teeth as a selenium biomonitor.Dietary exposure to mercury from the consumption of beluga was compared between historic and modern Mackenzie Delta Inuit populations, based on measured mercury concentrations in archeological beluga teeth and modern beluga tissues.Despite higher mercury levels in modern beluga, estimated average mercury exposure from the consumption of beluga is higher for pre-industrial Inuit populations than for modern Inuit populations, due to the significantly decreased average consumption of beluga among the modern population.students at Laurentian University for welcoming me into their lab and helping with my laboratory analysis.Donna Leggee, lab manager for CINE, also receives my heartfelt thanks for her support throughout the progression of my research.I would also like to acknowledge the students and research assistants working at CINE, providing endless insight, encouragement, and camaraderie.Furthermore, I would like to thank my family and friends for their laughter and support during the past two years.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".