Mapping of developmental origins of health and disease to ‘Sustainable Development Goals’ and implications for public health in Africa
Bibliographic record
Abstract
Goal 1 -End poverty in all its forms everywhere.*Goal 2 -End hunger, achieve food security and improved nutrition and promote sustainable agriculture.*Goal 3 -Ensure healthy lives and promote wellbeing for all at all ages.*Goal 4 -Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.*Goal 5 -Achieve gender equality and empower all women and girls.*Goal 6 -Ensure availability and sustainable management of water and sanitation for all.*Goal 7 -Ensure access to affordable, reliable, sustainable and modern energy for all.Goal 8 -Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all.Goal 9 -Build resilient infrastructure, promote inclusive and sustainable industrialisation and foster innovation.Goal 10 -Reduce inequality within and among countries.*Goal 11 -Make cities and human settlements inclusive, safe, resilient and sustainable.*Goal 12 -Ensure sustainable consumption and production patterns.Goal 13 -Take urgent action to combat climate change and its impacts.*Goal 14 -Conserve and sustainably use the oceans, seas and marine resources for sustainable development.Goal 15 -Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.Goal 16 -Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels.Goal 17 -Strengthen the means of implementation and revitalise the Global Partnership for Sustainable Development.*UN Sustainable Development Goals directly relatable to DOHaD marked with an asterisk 10 The Federal Council of Switzerland.n.d.2030 Agenda for Sustainable Development.Switzerland around the world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".