Climate change impacts on dietary nutrient status of Inuit in Nunavut, Canada
Bibliographic record
Abstract
Introduction: Traditional food (TF) is locally derived food harvested from the environment, which gives vital sustenance to an Arctic Indigenous People, the Inuit. TF contributes significantly to daily required nutrients. The Arctic is experiencing rapid climate change which is affecting access and availability of TF and potentially nutrient intake for Inuit. Objective: To characterizes the nutritional implications of climate change related to the TF system of Inuit in Nunavut, Canada. Methods: Two‐day focus groups and a frequency survey of 12 TF species were carried out in two Nunavut communities to record climate change observations and TF intake (g/day). Intake of 22 nutrients was compared to respective Dietary Reference Intakes. Results: Communities found climate change was affecting the TF harvest in both positive and negative ways. TF provided 100% of the Estimated Average Intakes for protein, vitamins A and B6, riboflavin, P, Fe, Cu, Zn and Se, and of the Adequate Intake for omega 3 fatty acids. Overall median daily intake of TF was 367 g/day and top species/parts were known wildlife foods. Conclusions: If climate change leads to increased TF harvest, nutrient intake has the potential to increase. Climate changes that may reduce access to TF may have serious consequences on dietary nutrient status of Inuit if no countermeasures are taken. Research Supported by: ArcticNet, Canada.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".