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Record W2106460171 · doi:10.3109/09637486.2012.676029

Frequency of consumption of foods and beverages by Inuvialuit adults in Northwest Territories, Arctic Canada

2012· article· en· W2106460171 on OpenAlexafffundabout
Francis Zotor, Tony Sheehy, Madalina Lupu, Fariba Kolahdooz, André Corriveau, Sangita Sharma

Bibliographic record

VenueInternational Journal of Food Sciences and Nutrition · 2012
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsAlberta HealthUniversity of Alberta
FundersAurora Research InstituteAmerican Diabetes Association
KeywordsConsumption (sociology)ArcticFood frequency questionnaireArctic charNutrientEnvironmental healthGeographyFood scienceMedicineBiologyEcology

Abstract

fetched live from OpenAlex

Limited data exist regarding nutrient intakes and overall dietary quality in Canadian Arctic populations. This cross-sectional study determined the frequency of consumption of traditional meats (e.g. caribou, polar bear, seal, char and whale) and non-traditional store-bought foods including non-traditional meats (e.g. beef, pork and chicken), grains, dairy, fruits, vegetables and non-nutrient dense foods (NNDFs) (e.g. butter, chocolate, chips, candy and pop) by Inuvialuit adults (175 women, mean age 44 ± 14 years; 55 men, mean age 41 ± 13 years) in three remote communities in the Northwest Territories. Using a validated quantitative food frequency questionnaire, frequency of consumption over a 30-day period was determined for 141 commonly reported foods. Mean consumption of traditional meats (1.6 times/day), fruits (1 time/day) and vegetables (0.6 times/day) was less frequent than that of NNDFs (5.0 times/day). Nutritional intervention strategies are needed to promote more frequent consumption of nutrient-rich foods and less frequent consumption of NNDFs in these Arctic communities.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.334
Teacher spread0.308 · 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

Citations13
Published2012
Admission routes3
Has abstractyes

Explore more

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