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Record W3032076063 · doi:10.15586/jptcp.v27i2.678

Overconsumption of sugar-sweetened beverages: Why is it difficult to control?

2020· article· en· W3032076063 on OpenAlexvenueno aff
Mohammed S. Razzaque

Bibliographic record

VenueJournal of Population Therapeutics and Clinical Pharmacology · 2020
Typearticle
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsnot available
FundersAl-Farabi Kazakh National UniversityUniversity of RwandaHarvard School of Dental Medicine
KeywordsOverconsumptionSugarFood scienceEconomicsChemistryMicroeconomics

Abstract

fetched live from OpenAlex

The consumption of sugar-sweetened beverages is a known contributory factor of childhood obesity that is documented around the globe. More importantly, reducing the consumption of sugar-sweetened beverages could reduce weight gain among overweight or obese children. Although sugar is present in many natural foods, artificial sugar is added into sugar-sweetened beverages, which has little or no nutritional value. However, the calories obtained from the sugar-sweetened beverages are linked to overweight and obesity, and an increase serving sizes of sugar-sweetened beverages over the years partly contributed to the alarming rise of childhood obesity around the globe. The sugar-sweetened beverages not only contain a high amount of sugar, but also contain a high amount of phosphate, and the possibility exists for an enhancing dual adverse health effects of sugar and phosphate. Increasing health awareness and limiting marketing approaches targeted towards the younger population are essential to reduce long-term health burdens that are linked to the overconsumption of sugar-sweetened beverages.

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 score0.176
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.105
GPT teacher head0.434
Teacher spread0.329 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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