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Record W2994998897 · doi:10.3390/nu12010034

Sodium Content in Commonly Consumed Foods and Its Contribution to the Daily Intake

2019· article· en· W2994998897 on OpenAlexfundno aff
Sonia Rosario Calliope, Norma Sammán

Bibliographic record

VenueNutrients · 2019
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsnot available
FundersConsejo Nacional de Investigaciones Científicas y TécnicasInternational Development Research Centre
KeywordsFood scienceEnvironmental healthPopulationSodiumFood composition dataPublic healthConsumption (sociology)Nutrition LabelingFood processingMedicineToxicologyBusinessChemistryBiology

Abstract

fetched live from OpenAlex

Salt consumption in many countries of the world exceeds the level recommended by WHO (5 g/day), which is associated with negative effects on health. Public health strategies to achieve the WHO's objectives include salt content monitoring, improved nutritional labelling and product reformulation. This study aimed to determine the sodium content in street food (SF), fast foods (FF) and artisanal foods (AF) of the Northwest of Argentina, which is not regulated. Moisture, ash and sodium were determined according to the Official Methods of Analysis (AOAC) in 189 samples from each of the three categories. The average and range values were: SF 520 (R: 74-932); FF 599 (R: 371-1093) and AF 575 (R: 152-1373) mg Na/100 g. Thus, general sodium content is high, which means that the consumption of a serving from most of the studied foods leads to an individual exceeding the recommended daily intake values. This study contributes to the knowledge of sodium content in evaluated foods and its contribution to the population intake. This reinforces the importance of implementing new public policies and regulations, advising consumers to check food nutritional labels andselect foods lower in salt content, raising food manufacturers' awarenessabout the importance of reducing sodium content in foods they produce and in public health.

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 score0.171
Threshold uncertainty score0.555

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 head0.039
GPT teacher head0.292
Teacher spread0.253 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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