Bibliographic record
Abstract
Over 17 000 children under the age of 5 years die every day.1 The majority of these children die in either sub-Saharan Africa or South Asia. Most of these children die from preventable causes, including gastroenteritis, pneumonia and malaria. Progress has been made in reducing child mortality with a 50% reduction in under five mortality between 1990 and 2013.1 This was however less than the two-thirds reduction target in the Millennium Development Goals. Although the majority of deaths occur in low-income and middle-income countries, there are wide disparities in child mortality between high-income countries. Child mortality rates in the UK are higher than in many other European countries.2 Similarly, the USA has higher child mortality rates than neighbouring Canada and Cuba.1 Additionally within countries, there are wide disparities in child mortality. The poorest, most deprived sections of the community have the highest child mortality rates. The new United Nations Sustainable Development Goals (SDGs) include the reduction of poverty and inequalities as they recognise the link between socioeconomic factors and health. The evidence base for ensuring children are healthy is quite extensive. Politicians and governments are aware that the following are key contributors to ensuring that children are healthy. They are all included within the SDGs.
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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.020 | 0.036 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".