Microbiota in foods from Inuit traditional hunting
Bibliographic record
Abstract
The foods we eat contain microorganisms that we ingest alongside the food. Industrialized food systems offer great advantages from a safety point of view, but have also been accused of depleting the diversity of the human microbiota with negative implications for human health. In contrast, artisanal traditional foods are potential sources of a diverse food microbiota. Traditional foods of the Greenlandic Inuit are comprised of animal-sourced foods prepared in the natural environment and are often consumed raw. These foods, some of which are on the verge of extinction, have not previously been microbiologically characterized. We mapped the microbiota of foods stemming from traditional Inuit land-based hunting activities. The foods included in the current study are dried muskox and caribou meat, caribou rumen and intestinal content as well as larval parasites from caribou hides, all traditional Inuit foods. This study shows that traditional drying methods are efficient for limiting microbial growth through desiccation. The results also show the rumen content of the caribou to be a highly diverse source of microbes with potential for degradation of plants. Finally, a number of parasites were shown to be included in the biodiversity of the assessed traditional foods. Taken together, the results map out a diverse source of ingested microbes and parasites that originate from the natural environment. These results have implications for understanding the nature-sourced traditional Inuit diet, which is in contrast to current day diet recommendations as well as modern industrialized food systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".