Trends of Energy and Macronutrients Intakes in Jordan as Obtained by Household Expenditure and Income Surveys
Bibliographic record
Abstract
Jordan has encountered changes in demographic and food consumption patterns during the last few decades as a result of the nutrition transition and westernized food habits. This paper aims to evaluate the trends in energy and macronutrient intakes in Jordan based on the data of Jordan Household Expenditure and Income Surveys (JHEIS) 1992-2010. The amounts of consumed food items were analyzed to estimate the intakes of energy and macronutrients for different governorates using (Food Processor SQL Nutrition and Fitness Software, 2010). The average estimated annual per capita intake (kg) for different food groups in the 2010 survey was the lowest since 1992 for legumes and oils and fats, whereas it was the highest for dairy products and eggs. The 2010 percentage of energy contribution of the food groups was the highest for meat and poultry, fish, and dairy products and eggs; whereas it was the lowest for grains, legumes, and fruits and vegetables. Additionally, there was a trend of increased energy intake in 2010 in comparison with previous JHEIS data. Energy intake of Jordanians has increased in 2010 as compared with average energy intake obtained in previous JHEIS surveys since 1992. In addition, the consumption of foods of animal-origin was increased, whereas the consumption of foods of plant-origin was decreased.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".