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Record W174498169 · doi:10.1096/fasebj.20.5.a1052-a

Maximum levels of vitamins and minerals for safe addition to foods in the current diets of Koreans

2006· article· en· W174498169 on OpenAlexaboutno aff
Se‐Young Oh, Hae‐Rang Chung, Mira Jun, Haewon Bae

Bibliographic record

VenueThe FASEB Journal · 2006
Typearticle
Languageen
FieldNursing
TopicNutrition, Health and Food Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsFortificationMicronutrientFortified FoodNiacinFood fortificationMedicineRiboflavinFood scienceEuropean unionEnvironmental healthVitamin B12BiologyBusiness

Abstract

fetched live from OpenAlex

Significant subgroups of Koreans were at risk of suboptimal intake of vitamins and minerals. Voluntary fortification was suggested to be an important means of alleviating micronutrient deficiencies. This study was conducted to estimate the levels of vitamins and minerals that can be safely added to foods by voluntary fortification. One day diet record data of 2,201 adult males aged 20–50 years drawn from the 2001 national nutrition survey were analyzed while considering the guidelines of the Institute of Medicine and the experiences by European Union (EU) and Canada. Selected food vehicles for voluntary fortification included all foods except for fresh non processed foods, alcoholic beverages, and widely used standardized staple foods and seasonings. The mean proportion of dietary energy from all potentially fortifiable foods was about 30%. The fractions of foods in the market available for fortification were assumed to be 5, 10, 25, 50, and 100%. With all of the fractions assumed, maximum levels of the addition of micronutrients to foods were greater than 100% Korean Recommended Dietary allowances (KRDAs) for vitamins B 12 , B 1 , niacin, B 2 , B 6 , E and C. With fortification of 50% of all potentially fortifiable foods, the maximum levels of the addition ranged 40–60% of the KRDAs for calcium, folic acid, iron and zinc. Results of this study suggest a wide range of micronutrients added safely to foods in the current diets of Koreans. (Supported by a grant from the Korean Food and Drug Administration, 2005)

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.043
GPT teacher head0.325
Teacher spread0.282 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2006
Admission routes1
Has abstractyes

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