East-Greenland traditional nutrition: a reanalysis of the Inuit energy balance and the macronutrient consumption from the Høygaard nutritional data (1936-1937)
Bibliographic record
Abstract
Greenlandic traditional nutrition was unique in the arctic environment because it was an almost exclusive meat dietary pattern. Høygaard et al. left Copenhagen in August 1936, and stayed in East Greenland until August 1937. The four members of the expedition resided in Tasisaq and visited eight settlements around where nutritional intake was recorded by residing in families. However, the nutritional intake was analysed on a household level. The aim of the present study is to reanalyse the Høygaard et al. data according to modern scientific standards.In total, 21 males and 14 females participated. Median (IQR) energy consumption was 3881 (1568) kcal.day−1 for males and 2910 (882) kcal.day−1 for females. Without the five participants living near trading centres, this was 3268 (219) kcal.day−1 and 2634 (723) kcal.day−1, respectively. Expressed in energy-percent, the macronutrient consumptions were 34% for protein, 37% for fat and 29% for carbohydrates. Without imported food, this was 41%, 49% and 10%, respectively.The main findings of the present study are, as expected, that the food consumed came mainly from traditional hunting, was low in plant foods and extremely low in carbohydrates. The Inuit succeeded to stay in apparently healthy conditions with a traditional meat-based dietary pattern.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".