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Record W2522048503 · doi:10.3390/nu8090582

Total and Free Sugar Content of Canadian Prepackaged Foods and Beverages

2016· article· en· W2522048503 on OpenAlexafffundabout
Jodi T. Bernstein, Alyssa Schermel, Christine Marie Mills, Mary R. L’Abbé

Bibliographic record

VenueNutrients · 2016
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of TorontoCanadian Stroke Network
KeywordsFood scienceSugarBusinessChemistry

Abstract

fetched live from OpenAlex

A number of recommendations for policy and program interventions to limit excess free sugar consumption have emerged, however there are a lack of data describing the amounts and types of sugar in foods. This study presents an assessment of sugar in Canadian prepackaged foods including: (a) the first systematic calculation of free sugar contents; (b) a comprehensive assessment of total sugar and free sugar levels; and (c) sweetener and free sugar ingredient use, using the University of Toronto’s Food Label Information Program (FLIP) database 2013 (n = 15,342). Food groups with the highest proportion of foods containing free sugar ingredients also had the highest median total sugar and free sugar contents (per 100 g/mL): desserts (94%, 15 g, and 12 g), sugars and sweets (91%, 50 g, and 50 g), and bakery products (83%, 16 g, and 14 g, proportion with free sugar ingredients, median total sugar and free sugar content in Canadian foods, respectively). Free sugar accounted for 64% of total sugar content. Eight of 17 food groups had ≥75% of the total sugar derived from free sugar. Free sugar contributed 20% of calories overall in prepackaged foods and beverages, with the highest at 70% in beverages. These data can be used to inform interventions aimed at limiting free sugar consumption.

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 score0.034
Threshold uncertainty score0.995

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 head0.030
GPT teacher head0.236
Teacher spread0.206 · 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

Citations122
Published2016
Admission routes3
Has abstractyes

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