The microbial composition of dried fish prepared according to Greenlandic Inuit traditions and industrial counterparts
Bibliographic record
Abstract
The practices of preparing traditional foods in the Arctic are rapidly disappearing. Traditional foods of the Arctic represent a rarity among food studies in that they are meat-sourced and prepared in non-industrial settings. These foods, generally consumed without any heating step prior to consumption, harbor an insofar undescribed microbiome. The food-associated microbiomes have implications not only with respect to disease risk, but might also positively influence host health by transferring a yet unknown diversity of live microbes to the human gastrointestinal tract. Here we report the first study of the microbial composition of traditionally dried fish prepared according to Greenlandic traditions and their industrial counterparts. We show that dried capelin prepared according to traditional methods have microbiomes clearly different from industrially prepared capelin, which also have more homogenous microbiomes than traditionally prepared capelin. Interestingly, the locally preferred type of traditionally dried capelin, described to be tastier than other traditionally dried capelin, contains bacteria that potentially confer distinct taste. Finally, we show that dried cod have comparably more homogenous microbiomes when compared to capelin and that in general, the environment of drying is a major determinant of the microbial composition of these indigenous food products.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".