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Record W4404014150 · doi:10.1186/s42779-024-00255-1

Fermented marine foods of the indigenous arctic people (Inuit) and comparisons with Asian fermented fish

2024· article· en· W4404014150 on OpenAlexaboutno aff
James W. Daily, Sunmin Park

Bibliographic record

VenueJournal of Ethnic Foods · 2024
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsFermented fishFermentation in food processingIndigenousArcticFermentationFish <Actinopterygii>Fish productsThe arcticFisheryGeographyFood scienceBiologyEcologyGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract The Inuit, sometimes referred to as Eskimos, are indigenous people to the remote circumpolar regions of the northern hemisphere that remain relatively inaccessible to outsiders. The traditional diet consisted almost entirely of raw animal foods eaten fresh, dried, or fermented and was similar to the diets of wild carnivorous animals. From the 1950s onward, the Inuits gradually adopted Western foods. With the adoption of a more Western diet, there has also been a corresponding increase in Western diseases such as obesity, type 2 diabetes, hypertension, and some cancers. Asians have also consumed salted fermented fish, but the fermented fish are different due to environmental temperatures. Although the microbial content of Inuit fermented foods is uniquely different from that of Asian fermented foods, Asian and Inuit fermented foods appear to be similarly important for supporting gut and immune health. The benefits of Asian fermented fish for improving the biodiversity of the microbiome and the generation of bioactive amines from proteins may be similar to the fermented marine foods of the Inuits. This study reviewed traditional fermented fish consumed by the Inuit people and Asians, highlighting various aspects that can offer valuable insights into the nutritional, cultural, and health dimensions of these practices.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.366
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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