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Record W4282920772 · doi:10.3390/nu14102165

Comment on Muzzioli et al. Are Front-of-Pack Labels a Health Policy Tool? Nutrients 2022, 14, 771

2022· review· en· W4282920772 on OpenAlexaff
Hassan Aguenaou, Nancy Babió, Mélanie Deschasaux, Pilar Galán, Serge Herçberg, Chantal Julia, Alexandra Jones, Γεώργιος Καρπέτας, Bridget Kelly, Emmanuelle Kesse‐Guyot, Lamprini Kontopoulou, Marie‐Ève Labonté, Cliona Ní Mhurchú, Igor Pravst, Simone Pettigrew, Elio Ríboli, Jordi Salas‐Salvadó, Bernard Srour, Mathilde Touvier, Stefanie Vandevijvere

Bibliographic record

VenueNutrients · 2022
Typereview
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFront (military)Nutrition facts labelNarrativeNutrition LabelingLabellingPublishingPolitical scienceMedicineSociologyFood scienceChemistryArtEngineeringSocial scienceLiteratureLawMechanical engineering

Abstract

fetched live from OpenAlex

As scientists working and publishing in the field of front-of-pack nutrition labelling (FOPNL) for many years, we have read with interest and concern the narrative review regarding their effectiveness by Muzzioli et al. [...].

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 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)0.020
metaresearch head score (Gemma)0.100
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.034
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.100
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0030.003
Science and technology studies0.0030.004
Scholarly communication0.0040.007
Open science0.0080.003
Research integrity0.0340.034
Insufficient payload (model declined to judge)0.0150.017

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 head0.092
GPT teacher head0.406
Teacher spread0.314 · 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
GenreCommentary

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

Citations3
Published2022
Admission routes1
Has abstractyes

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