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Record W2746591945 · doi:10.3390/nu9080884

Characterization of Breakfast Cereals Available in the Mexican Market: Sodium and Sugar Content

2017· article· en· W2746591945 on OpenAlexfundno aff
Claudia Nieto, Sofia Rincón‐Gallardo Patiño, Lizbeth Tolentino‐Mayo, Ángela Carriedo, Sı́món Barquera

Bibliographic record

VenueNutrients · 2017
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
FundersInternational Development Research CentreBloomberg PhilanthropiesBloomberg Family Foundation
KeywordsSugarFood scienceChemistryBiotechnologyBiology

Abstract

fetched live from OpenAlex

Preschool Mexican children consume 7% of their total energy intake from processed breakfast cereals. This study characterized the nutritional quality and labelling (claims and Guideline Daily Amount (GDA)) of the packaged breakfast cereals available in the Mexican market. Photographs of all breakfast cereals available in the 9 main food retail chains in the country were taken. The nutrition quality of cereals was assessed using the United Kingdom Nutrient Profiling Model (UKNPM). Claims were classified using the International Network for Food and Obesity/non-communicable Diseases Research, Monitoring and Action Support (INFORMAS) taxonomy and the GDA was defined according to the Mexican regulation, NOM-051. Overall, a total of 371 different breakfast cereals were analysed. The nutritional profile showed that 68.7% were classified as “less healthy”. GDAs and claims were displayed more frequently on the “less healthy” cereals. Breakfast cereals within the “less healthy” category had significantly higher content of energy, sugar and sodium (p < 0.001). Most of the claims were displayed in the “less healthy” cereals (n = 313). This study has shown that there is a lack of consistency between the labelling on the front of the pack and the nutritional quality of breakfast cereals.

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 score0.047
Threshold uncertainty score0.171

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 head0.045
GPT teacher head0.277
Teacher spread0.232 · 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

Citations28
Published2017
Admission routes1
Has abstractyes

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