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Record W2601980005

Novel method to achieve price-optimized, fully nutritious, health-promoting and acceptable national food baskets

2015· article· en· W2601980005 on OpenAlexaboutno aff
Alexandr Parlesak, Aileen Robertson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
Fundersnot available
KeywordsHealth foodBusinessAgricultural economicsEnvironmental economicsEconomicsFood scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

Purpose: The purpose of this study was to generate a framework for the development of health-promoting, fully nutritious, socially acceptable, and affordable national food baskets to be used as an advocacy tool by governments. In addition to containing all (micro-)nutrient requirements, food baskets should also reflect dietary guidelines to prevent non-communicable diseases and be optimized to achieve the highest possible social acceptance. So far, integrative approaches that include all these aspects are lacking. Methods: Food composition, local availability, food prices, national and international recommendations on ‘healthy’ nutrition, and current respective preferences of the corresponding populations were optimized using linear programming (LP) methods (Dantzig’s simplex algorithm). The desired outcomes were ‘best-for-price’ solutions (= food baskets) from a list of 150-190 locally available foods. The study was designed to obtain healthy, affordable, and socially acceptable diets for three European countries (Denmark, Slovenia, and Romania) and in three regions within Canada, Argentina, and Switzerland. Moreover, the costs for the “limiting” micronutrients and relative price increases were calculated after including a range of constraints (e.g. dietary recommendations vs nutrient requirements; wider range of biodiversity (increased number of foods included) and social acceptability). All data were collected in the respective countries using standard methodology. Results: Key micronutrients influencing the increased cost of food baskets were calcium, potassium, and the vitamins A, B2, C, and D. When additional constraints were applied by integrating food-based dietary guidelines and social acceptability (as measured by current consumption patterns, central 80% percentile), the cost increased by approximately one third and three fold, respectively. The wider range of biodiversity resulted in just minor increases in cost. Conclusions: The cost of health-promoting, fully nutritious, and socially acceptable food baskets depended primarily on their adaptation to local dietary patterns. Fully nutritious and health-promoting food baskets can be achieved at a relatively low price. However if these are not socially acceptable to the target population, the use of a framework using linear programming based solely on nutritional values seems limited.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.075
GPT teacher head0.360
Teacher spread0.285 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2015
Admission routes1
Has abstractyes

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