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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 OpenAlex
Jayne V. Woodside, Petra Klassen Wigger, Philippe Legrand, Ronald P. Mensink, Dariush Mozaffarian, John L. Sievenpiper

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.375

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 head0.009
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