Bibliographic record
Abstract
An adequate intake of folate during pregnancy, lactation, and infancy is essential for maternal and child health and normal growth. Higher folate requirements during pregnancy and lactation are difficult to meet by increased intake of folate-rich food products only. Supplementation with folic acid is recommended not only to meet the higher requirements but also to prevent adverse pregnancy outcomes such as neural tube defects (NTDs). In countries that have implemented food fortification with folic acid, the folate intake has raised but does not yet meet the recommended amount for NTD risk reduction. Women's awareness of the need to supplement with folic acid prior to conception shall be raised in all countries. It is under debate whether a high folic acid intake might have metabolic and functional effects in utero and for the infant. Research is needed to investigate potential alternative folate forms for food fortification programs and to test their efficacy in risk reduction of adverse pregnancy outcomes. Breast-fed infants most likely receive sufficient folate. While the folate level of human milk is simulated in infant formula, data are lacking on the bioavailability and effect of folic acid in infants and on whether a tolerable upper intake level should be defined.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".