What's in your freezer? Traditional food use and food security in two Yukon First Nations communities.
Bibliographic record
Abstract
Traditional foods are central to the well-being of Aboriginal communities however there is a trend of decreasing traditional food use which can pose health risks when the replacement foods are low in nutrients and high in carbohydrates and saturated fats. This study collected information on the frequency and quantity of traditional food consumption as well the level of food security of 29 adults in the Vuntut Gwitchin First Nation community of Old Crow and 33 adults in the Tlingit community of Teslin, both of Yukon, Canada. In each community traditional foods were shown to be an important part of the diet, although challenges in access to and availability of foods were reported. Chemical contamination is another challenge in the context of food security. There is limited data on mercury levels in caribou, a principal food source for the Vuntut Gwitchin First Nation. Seventy-five caribou muscle, 63 kidney and 3 liver samples were analyzed for total mercury and combined with reported dietary information to calculate estimated total mercury and methylmercury exposure. Nutrient intake was calculated by combining data from the Canada Nutrient File and supplemented by Kuhnlein et al. (2006) and Hidiroglou et al., (2008) with the collected dietary information. Caribou issues were found to contribute high levels of important nutrients to the diet and pose minimal health risk from mercury exposure.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".