Modeling thiamine fortification: a case study from Kuria atoll, Republic of Kiribati
Bibliographic record
Abstract
In 2014, there was an outbreak of beriberi on Kuria, a remote atoll in Kiribati, a small Pacific Island nation. A thiamine-poor diet consisting mainly of rice, sugar, and small amounts of fortified flour was likely to blame. We aimed to design a food fortification strategy to improve thiamine intakes in Kuria. We surveyed all 104 households on Kuria with a pregnant woman or a child 0-59 months. Repeat 24-h dietary recalls were collected from 90 men, 17 pregnant, 44 lactating, and 41 other women of reproductive age. The prevalence of inadequate thiamine intakes was >30% in all groups. Dietary modeling predicted that rice or sugar fortified at a rate of 0.3 and 1.4 mg per 100 g, respectively, would reduce the prevalence of inadequate thiamine intakes to <2.5% in all groups. Fortification is challenging because Kiribati imports food from several countries, depending on price and availability. One exception is flour, which is imported from Fiji. Although resulting in less coverage than rice or sugar, fortifying wheat flour with an additional 3.7 mg per 100 g would reduce the prevalence of inadequacy to under 10%. Kiribati is small and has limited resources; thus, a regional approach to thiamine fortification is needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".