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Record W2739161645 · doi:10.1111/nyas.13417

International summit on the nutrition of adolescent girls and young women: consensus statement

2017· article· en· W2739161645 on OpenAlexaff
Nancy F. Krebs, Susan P. Bagby, Zulfiqar A Bhutta, Kathryn G. Dewey, Caroline Fall, Fred Gregory, William W. Hay, Lisa Rhuman, Christine Wallace Caldwell, Kent L. Thornburg

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2017
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsSickKids FoundationCentre for Global Health ResearchHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesMedical Research CouncilBill and Melinda Gates Foundation
KeywordsSummitStatement (logic)Political scienceMedicinePediatricsPsychologyGeographyLaw

Abstract

fetched live from OpenAlex

An international summit focusing on the difficult challenge of providing adequate nutrition for adolescent girls and young women in low- and middle-income countries was held in Portland, Oregon in 2015. Sixty-seven delegates from 17 countries agreed on a series of recommendations that would make progress toward improving the nutritional status of girls and young women in countries where their access to nutrition is compromised. Delegate recommendations include: (1) elevate the urgency of nutrition for girls and young women to a high international priority, (2) raise the social status of girls and young women in all regions of the world, (3) identify major knowledge gaps in the biology of adolescence that could be filled by robust research efforts, (4) and improve access to nutrient-rich foods for girls and young women. Attention to these recommendations would improve the health of young women in all nations of the world.

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 imitation

Not 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.

metaresearch head score (Codex)0.078
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.078
Threshold uncertainty score0.413

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0780.051
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0030.002
Science and technology studies0.0040.003
Scholarly communication0.0060.005
Open science0.0070.014
Research integrity0.0210.030
Insufficient payload (model declined to judge)0.0070.004

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.

Opus teacher head0.111
GPT teacher head0.367
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations24
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the New York Academy of SciencesSame topicChild Nutrition and Water AccessFrench-language works237,207