Nutrition biomarker assessment and exploration of the role of country foods to improve food security in the Sahtú Region, Canada
Bibliographic record
Abstract
Country foods (i.e. wild traditional food) are associated with improved nutrition for northern populations. In response to community concerns, a project was implemented from 2019 to 2021 in the Sahtú region, Northwest Territories, Canada, to: 1) analyse nutrition biomarkers (vitamins A, B1, B2, B6, B12, D, E, folate, P, Na) in blood samples, in order to assess nutritional status and identify nutrient deficiencies, and 2) use a survey to document how access to country foods may improve food security in the community of Tulı́t'a. Findings from the nutritional biomarker assessments (n = 128) indicated that 94% of participants experienced clinical vitamin D deficiency (<20 ng/L of plasma 25-hydroxy-vitamin D3) and 9% had folate deficiency (<8.7 nmol/L total folate). In the previous 12 months, 71% of participants did not always have money to get more food when needed, but 92% of participants said they were not left hungry. Country foods were used to increase the quality or quantity of the diet. Increasing country food consumption, such as fatty fish and large game meat and organs could mitigate the vitamin D and folate deficiencies. Policies should be implemented to improve food security in the North by facilitating access to country food.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".