Effects of preparation on nutrient and environmental contaminant levels in Arctic beluga whale (Delphinapterus leucas) traditional foods
Bibliographic record
Abstract
For Canadian Arctic indigenous populations, marine mammal (MM) traditional foods (TFs) represent sources of both important nutrients and hazardous environmental contaminants. Food preparation is known to impact the nutrient and environmental contaminant content of processed items, yet the impacts of preparation on indigenous Arctic MM TFs remain poorly characterized. In order to determine how the various processes involved in preparing beluga blubber TFs affect their levels of nutrients and environmental contaminants, we collected blubber samples from 2 male beluga whales, aged 24 and 37 years, captured during the 2014 summer hunting season in Tuktoyaktuk, Northwest Territories, and processed them according to local TF preparation methods. We measured the levels of select nutrients [selenium (Se), polyunsaturated fatty acids (PUFAs)] and contaminants [organochlorine pesticides, perfluoroalkyl and polyfluoroalkyl substances (PFASs), polybrominated diphenyl ethers, polychlorinated biphenyls, polycyclic aromatic hydrocarbons (PAHs), mercury (Hg)] in raw and prepared (boiled, roasted, aged) beluga blubber TFs. The impacts of beluga blubber TF preparation methods on nutrient and environmental contaminant levels were inconsistent, as the majority of processes either did not appear to influence concentrations or affected the two belugas differently. However, roasting and ageing beluga blubber consistently impacted certain compounds: roasting blubber increased concentrations of hydrophilic substances (Se and certain PFASs) through solvent depletion and deposited PAHs from cookfire smoke. The solid-liquid phase separation involved in ageing blubber depleted hydrophilic elements (Se, Hg) and some ionogenic PFASs from the lipid-rich liquid oil phase, while PUFA levels appeared to increase, and hydrophobic persistent organic pollutants were retained. Ageing blubber adjacent to in-use smokehouses also resulted in considerable PAH deposition to processed samples. Our findings demonstrated that contaminant concentration differences were greater between the two sets of whale samples, based on age differences, than they were within each set of whale samples, due to variable preparation methods. When considering means to minimize human contaminant exposure while maximizing nutrient intake, consumption of aged liquid from younger male whales would be preferred, based on possible PUFA enhancement and selective depletion of hydrophilic environmental contaminants in this food item.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".