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Record W2917848945 · doi:10.3390/nu11030526

Differences in the Quantity and Types of Foods and Beverages Consumed by Canadians between 2004 and 2015

2019· article· en· W2917848945 on OpenAlexafffundabout
Claire N. Tugault-Lafleur, Jennifer Black

Bibliographic record

VenueNutrients · 2019
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsFood scienceDairy foodsRefined grainsWhole grainsFood groupSugarFood guidePopulationEnvironmental healthOrange juiceAdded sugarMedicineBiology

Abstract

fetched live from OpenAlex

This study examined differences in food and beverage intake estimated from nationally representative surveys of Canadians in 2004 and 2015 collected through the Canadian Community Health Surveys. Differences in mean daily energy intake and amounts of food consumed were compared between 2004 and 2015 and across age groups for all energy reporters (aged 2 years+) and among only plausible energy reporters. From 2004 to 2015, mean energy intake decreased by 228 kcal/day (all energy reporters) and 74 kcal/day (plausible energy reporters). Canadians reported consuming more daily servings of meat and alternatives but fewer servings of vegetables and fruit and milk and alternatives in 2015 compared to 2004. Analyses of food subgroups revealed that Canadians reported consuming more daily servings of dark green and orange vegetables, dairy products, legumes, nuts and seeds, and eggs but fewer servings of potatoes, other vegetables, fruit juices, fluid milk, and sugar-sweetened beverages in 2015 compared to 2004. While some aspects of the Canadian diet have improved, daily mean intake of other nutritious foods either stagnated or worsened over time. Continued attention is needed to improve population-level intakes of vegetables, fruit, whole grains, and protein foods such as legumes, nuts, seeds, and lower fat dairy products.

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)0.000
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 score0.008
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.020
GPT teacher head0.269
Teacher spread0.250 · 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

Citations76
Published2019
Admission routes3
Has abstractyes

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