Bibliographic record
Abstract
From the December 2009 supplement, Multiple Micronutrient Supplementation During Pregnancy in Developing Country Settings, the following figures should have been published as follows: In, “Multiple micronutrient supplementation during pregnancy in low-income countries: A meta-analysis of effects on stillbirths and on early and late neonatal mortality” by Carine Ronsmans, David J. Fisher, Clive Osmond, Barrie M. Margetts, Caroline H. D. Fall, and the Maternal Micronutrient Supplementation Study Group (MMSSG) Page 552 — Figure 5. Random effects model forest plots for effects of multiple micronutrient supplementation on early neonatal deaths. CI, confidence interval; ES, effect size. In, “Multiple micronutrient supplementation during pregnancy in developing-country settings: Policy and program implications of the results of a meta-analysis” by Roger Shrimpton, Sandra L. Huffman, Elizabeth R. Zehner, Ian Darnton-Hill, and Nita Dalmiya Page 562 — Figure 1. Average adherence to supplementation in the meta-analysis studies In, Special Issue: Research from the Institute of Nutrition of Central America and Panama (INCAP) 1949–1999, Volume 31, Number 1: The introductory article by Nevin S. Scrimshaw entitled “The Origin and Development of INCAP” inadvertently omitted some key words on page 6, second column, first paragraph. It was Fernando Viteri, who after an outstanding research career at INCAP, “… became the head of food and nutrition at PAHO/WHO, Washington and later a full professor at the University of California, Berkeley and Scientist at the Children's Hospital Oakland Research Institute.”
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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.536 | 0.389 |
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".