Evaluation of Nutritional Status of Rural Bengalee Primary School Boys (6-9 Years) in Comparison to Indian Children
Bibliographic record
Abstract
A cross sectional study was undertaken to determine nutritional status and growth pattern of 410 rural primary school boys (6-9 years of age) belong from lower socioeconomic status according Kuppuswamy's socioeconomic scale (2012) in West Midnapore District of West Bengal. The daily nutritional intake of the children was measured by weighing raw and cooked foods and also by 24 hrs recall method. The body mass indexes (BMI), body composition, protein/calory adequacy status, protein-energy ratio of the primary school boys were measured to assess the nutritional status. The food and nutrient intake of the subjects were compared with their respective Indian values (NNMB, 2002). The diet of rural school boys was found to be imbalanced with plenty intake of milk and lower in intake of cereals, pulses but higher in intake of vegetables than that of their Indian counterpart. It was observed that the diets are predominantly more deficient in calories than protein. Most of the boys had normal body weight and only a little number of boys was overweight or underweight and the protein energy ratio is lower than ICMR recommended value except the age group of 7 years and the maximum percentage (about 20%) of underweight boys is found in the age group of 9 years. It was further observed that there was a significant correlation between body composition and BMI (p<0.001).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".