Bibliographic record
Abstract
Nutritional insecurity has evolved as a serious global problem during the last few decades. The situation got worsen during the first quarter of the 21st century after the international food cost depression. It was first scrutinized in Millennium Development Goals (MDGs) signed in September 2000 and now in 17 Sustainable Development Goals (SDGs) set by the United Nation for 2030. Nutritional security has become an important topic for scientific community and regulatory bodies. According to a report published by FAO in 2017, the number of malnourished families increased worldwide in 2016 and the developing countries host the majority of this undernourished population. Although some reports suggested that the food hygiene condition in Pakistan has improved, but still lower than other South-East Asian countries. Estimates show malnutrition, an outcome of chronic food insecurity, annually costs Pakistan almost 3 per cent of its GDP in the form of lost productivity. It is a significantly high economic loss for a developing country like Pakistan. Therefore, efforts are needed to improve nutritional security in Pakistan by safeguarding both physical and economic access to high quality food for everyone.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".