Nutrition Science in India: Green leafy vegetables: A potent food source to alleviate micronutrient deficiencies
Bibliographic record
Abstract
Micronutrient Deficiencies (MNDs) are of great public health and socioeconomic importance worldwide. They affect low-income countries but are also a significant factor in health problems in industrialized societies with impacts among wide vulnerable groups in the population, including women, children, the middle-aged,and ederley. Nutrition deals with the intake of food, considered in relation to the nutritional andenergy needs of the individual. By consuming a healthy diet, many of the health problems can be avoided. The diet is largely determined by the perceived palatability of foods. Globally, majority of people are consuming consistently less than the daily recommended allowance requirement of the nutrient components. Even in developed countries like Australia, Canada, Europe, UK and USA researchers have concluded that there is large gap between actual and recommended consumption of both green leafy vegetables and fruits despite decades of concern and publicity. This gap is much more in developing countries including India. The principal nutritional problems in developing countries include protein-energy malnutrition (PEM), iodine deficiency, vitamin A deficiency (VAD) and iron deficiency anemia (IDA). A growing number of countries are confronted with new health risks linked to diet, namely cardiovascular diseases (CVD), diabetes, obesity and cancer. Nutrition science is being dominated by two conflicting observations since 1960. One is how to eat healthy and maintain a healthy body weight and the other hand rapidly increasing rates of obesity and diabetes suggest misconception about the conventional thinking. In 1960 fewer than 13 percent of Americans were obese and 1 percent was diabetes. The obese percentage was almost triple and diabetes has increased seven fold today. Mean while the research literature also has been ballooned on diabetes and obesity from 1960 to 2016. Though the green leafy vegetables are playing an important role to prevent the nutritional disorders, the extensive literature explored that there is a gap of knowledge in the appropriate consumption of GLV and its benefits to human. Further efforts should make to widen the knowledge in this unmapped area of research. This trend was observed in other nutritional disorders as well.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".