Assessing Food Security In Terms Of Nutrient Intake In Agri-Business Households (As Primary Source Of Income) In Bankura District Of West Bengal Of India
Bibliographic record
Abstract
This study evaluated food security and recommended policies to enhance calorie, protein, and fat intake in selected households. It surveyed 50 participants from the agri-business industry using Simple Random Sampling without Replacement and classified households based on income. Most family members were aged 26 to 45 years. The quantities of food consumed by the households were converted into equivalent amounts of calorie, protein, and fat based on the Nutrition Chart from an ICMR publication. The study converted food quantities to calorie, protein, and fat amounts using a Nutrition Chart, and adjusted calorie intake based on age and sex differences. Group-1 and Group-2 households were recommended to have balanced diets with more milk, pulses, fruits, and vegetables, and to increase protein intake. Protein intake was higher than the recommended amount for agri-business households, except for Group- 1 and Group-2. The study suggests policies to improve food intake, create awareness about balanced diets, and encourage the consumption of nutritious food, particularly in low-income households.
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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 | 0.001 |
| 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) | 0.001 | 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".