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Record W3159897817 · doi:10.3390/nu13051471

Free Sugars Consumption in Canada

2021· article· en· W3159897817 on OpenAlexaffabout
Huma Rana, Marie-Claude Mallet, Alejandro Gonzalez, Marie-France Verreault, Sylvie St‐Pierre

Bibliographic record

VenueNutrients · 2021
Typearticle
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsHealth Canada
Fundersnot available
KeywordsOverweightLimitingFood scienceObesityEnvironmental healthConsumption (sociology)MedicineNutrientChemistryEngineering

Abstract

fetched live from OpenAlex

Free sugars (FS) are associated with a higher risk of dental decay in children and an increased risk of weight gain, overweight and obesity and type 2 diabetes. For this reason, Canada's Food Guide recommends limiting foods and beverages that contribute to excess free sugars consumption. Estimating FS intakes is needed to inform policies and interventions aimed at reducing Canadians' consumption of FS. The objective of this study was to estimate FS intake of Canadians using a new method that estimated the free sugars content of foods in the Canadian Nutrient File, the database used in national nutrition surveys. We define FS as sugars present in food products in which the structure has been broken down. We found that 12% of total energy (about 56 g) comes from FS in the diet of Canadians 1 year of age and older (≥1 year). The top four sources were: (1) sugars, syrups, preserves, confectionary, desserts; (2) soft drinks; (3) baked products and (4) juice (without added sugars), and accounted for 60% of total free sugars intake. The results show that efforts need to be sustained to help Canadians, particularly children and adolescents, to reduce their FS intake.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.029
Threshold uncertainty score0.214

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.007
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.251
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations37
Published2021
Admission routes2
Has abstractyes

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