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Record W3154717121 · doi:10.1016/s2213-8587(21)00078-4

Towards unified and impactful policies to reduce ultra-processed food consumption and promote healthier eating

2021· review· en· W3154717121 on OpenAlexfundno aff
Barry M. Popkin, Sı́món Barquera, Camila Corvalán, Karen Hofman, Carlos Augusto Monteiro, Shu Wen Ng, Elizabeth C. Swart, Lindsey Smith Taillie

Bibliographic record

VenueThe Lancet Diabetes & Endocrinology · 2021
Typereview
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on AgingDivision of Research Capacity DevelopmentBloomberg Family FoundationSouth African Medical Research CouncilRobert Wood Johnson FoundationNational Institute of Diabetes and Digestive and Kidney DiseasesInternational Development Research CentreNational Institutes of HealthBloomberg PhilanthropiesLaura and John Arnold FoundationArnold Ventures
KeywordsEnvironmental healthMedicineAdded sugarMicronutrientConsumption (sociology)Food processingFood choiceFood policySaturated fatObesityBusinessMarketingFood scienceFood securityAgricultureGeography

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0090.002

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 head0.106
GPT teacher head0.390
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations338
Published2021
Admission routes1
Has abstractno

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