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Record W2961589517 · doi:10.2903/j.efsa.2019.e170719

Integrating nutrition science and consumer behaviour into future food policy

2019· article· en· W2961589517 on OpenAlexaff
Jayne V. Woodside, Petra Klassen Wigger, Philippe Legrand, Ronald P. Mensink, Dariush Mozaffarian, John L. Sievenpiper

Bibliographic record

VenueEFSA Journal · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsUniversity of Toronto
FundersEuropean Food Safety Authority
KeywordsFood scienceBusinessMarketingBiotechnologyBiology

Abstract

fetched live from OpenAlex

The session 'Advancing risk assessment science - Nutrition' at EFSA's third Scientific Conference 'Science, Food and Society' aimed to foster the ongoing debate on the extent to which single nutrients, whole foods and overall diets may impact human health in wealthy populations, and to explore how societal and technological developments could affect food choices and diets in the future. The overarching goal of the session was to discuss how dietary guidelines could evolve to account for the switch from single nutrient deficiencies to diseases of malnutrition in all its forms as the predominant public health concern in developed countries. Speakers addressed the contribution of single nutrients to the prevalence of chronic metabolic diseases, discussed the need to move towards diets focusing on whole foods and overall eating patterns, provided insides on food innovation and consumer behaviour and stressed the need for multidisciplinary approaches to face these challenges.

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)0.021
metaresearch head score (Gemma)0.021
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: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.010
Scholarly communication0.0140.012
Open science0.0010.005
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0100.001

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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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
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

Citations6
Published2019
Admission routes1
Has abstractyes

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