Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Women are doubly vulnerable to malnutrition, because of their high nutritional requirements for pregnancy and lactation and also because of gender inequalities in poverty. Undernutrition and overnutrition coexist in developing countries undergoing rapid nutrition transition, and women are susceptible to this double burden of "dysnutrition," often cumulating stunting or micronutrient malnutrition with obesity or other nutrition-related chronic diseases. The purpose of the present paper is to describe the adverse impact of income and gender inequities on women's nutritional health, and the dramatic consequences, not only for women themselves, but for children, families, and societies. Improving women's resources, including health, nutrition, education, and decisional power, is critical for equity and for the health of children and adults of future generations, since poor fetal and infancy nutrition is another risk factor for chronic diseases, in particular abdominal obesity, type 2 diabetes, hypertension, and cardiovascular disease. Addressing malnutrition and nutrition-related chronic diseases simultaneously is a challenge facing developing countries, and examples of promising initiatives are provided. Focusing on women along the lifecycle, according to the continuum of care approach, is essential to achieving the Millennium Development Goals and to breaking the intergenerational cycle of poverty, malnutrition, and ill-health.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it