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Record W110974713 · doi:10.1177/15648265030244s213

Role of Public-Private Partnership in Micronutrient Food Fortification

2003· review· en· W110974713 on OpenAlexaff
M.G. Venkatesh Mannar, Marc Van Ameringen

Bibliographic record

VenueFood and Nutrition Bulletin · 2003
Typereview
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsNutrition International
Fundersnot available
KeywordsBusinessFood fortificationPopulationMicronutrientContext (archaeology)Private sectorGeneral partnershipPublic healthPromotion (chess)MarketingEnvironmental healthEconomic growthMedicineEconomicsPoliticsPolitical science

Abstract

fetched live from OpenAlex

Iron, iodine, and vitamin A deficiencies prevent 30% of the world's population from reaching full physical and mental potential. Fortification of commonly eaten foods with micronutrients offers a cost-effective solution that can reach large populations. Effective and sustainable fortification will be possible only if the public sector (which has the mandate and responsibility to improve the health of the population), the private sector (which has experience and expertise in food production and marketing), and the social sector (which has grass-roots contact with the consumer) collaborate to develop, produce, and promote micronutrient-fortified foods. Food fortification efforts must be integrated within the context of a country's public health and nutrition situation as part of an overall micronutrient strategy that utilizes other interventions as well. Identifying a set of priority actions and initiating a continuous dialogue between the various sectors to catalyze the implementation of schemes that will permanently eliminate micronutrient malnutrition are urgently needed. The partners of such a national alliance must collaborate closely on specific issues relating to the production, promotion, distribution, and consumption of fortified foods. Such collaboration could benefit all sectors: National governments could reap national health, economic, and political benefits; food companies could gain a competitive advantage in an expanding consumer marketplace; the scientific, development, and donor communities could make an impact by achieving global goals for eliminating micronutrient malnutrition; and by demanding fortified foods, consumers empower themselves to achieve their full social and economic potential.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.055
GPT teacher head0.295
Teacher spread0.240 · 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 teacher head, not a consensus.

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

Citations15
Published2003
Admission routes1
Has abstractyes

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