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Record W2535613459 · doi:10.1016/s2214-109x(16)30264-9

Mapping the geography of child mortality: a key step in addressing disparities

2016· letter· en· W2535613459 on OpenAlexaff
Zulfiqar A Bhutta

Bibliographic record

VenueThe Lancet Global Health · 2016
Typeletter
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsKey (lock)Child mortalityGeographyMEDLINEMedicineEnvironmental healthData sciencePolitical sciencePopulationComputer scienceBiologyEcology

Abstract

fetched live from OpenAlex

A hallmark of the past decade has been the remarkable progress made in reducing maternal and child mortality, an achievement credited in some measure to the Millennium Development Goals. The reduction in child deaths from an estimated 12·7 million under-5 deaths in 1990 to fewer than 6 million deaths by 2015 is a remarkable achievement in global health. 1 The fact that this reduction is less than uniform and varies widely between countries and in populations within countries has also been well recognised.

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)0.021
metaresearch head score (Gemma)0.105
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.105
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0110.016
Science and technology studies0.0010.003
Scholarly communication0.0070.011
Open science0.0030.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0140.002

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 head0.061
GPT teacher head0.344
Teacher spread0.283 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations14
Published2016
Admission routes1
Has abstractyes

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