Development of Omani-branded food composition database for an electronic dietary assessment tool
Bibliographic record
Abstract
Background: The food composition database provides a comprehensive information on the various nutrients present in the foods. At present, Oman lacks a food composition database (FCDB) of locally produced branded foods, which necessitates the development of such a database. Objectives: The aim of this study is to develop an FCDB for Omani-branded foods available for local consumption for an electronic dietary assessment tool. Methods: Back-of-pack (BOP) nutritional information of these branded foods available in our markets was gathered from manufacturer data. Food mapping was used to match the branded foods according to the BOP macronutrient data and food description to the appropriate generic food item from United States Department of Agriculture (USDA) or Canadian FCDBs. Results: The developed database is composed of 571 food items with associated 60 nutrients. The majority of food items (91%) were mapped to a single generic food item. The rest (9%) was mapped to multiple generic food items. Overall, 96% of food items were mapped to a single generic food, which was matched based on BOP macronutrients and item descriptions. However, the minority (4%) were mapped based on item description alone as either the BOP nutrients were implausible. Moreover, 91% of food items were individually mapped to within 10% agreement with the generic food item for energy. The fish and fish group has the largest mean of absolute percentage difference in energy between BOP and generic items (16%). Conclusion: This currently developed database would critically help to accurately assess the dietary intake of the Omani population once incorporated into an electronic dietary assessment tool, and it can be updated in the future on a regular basis.
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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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".