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Record W4405582643 · doi:10.32725/jnss.2016.004

Strengthening nutrition-sensitive agriculture through bio fortification: introducing a comprehensive framework based on findings from a Canadian/Ethiopian project

2016· article· en· W4405582643 on OpenAlexafffundabout
Carol J. Henry, Patience Elabor-Idemudia, Sheleme Beyene, Susan J. Whiting, Nigatu Regassa

Bibliographic record

VenueJournal of Nursing Social Studies Public Health and Rehabilitation · 2016
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Saskatchewan
FundersGlobal Affairs Canada
KeywordsFortificationAgricultureBiotechnologyBusinessEnvironmental planningEngineeringGeographyFood scienceBiologyEcology

Abstract

fetched live from OpenAlex

This paper examines the links between agriculture and nutrition as it seeks to address food and nutrition security in Southern Ethiopia. It draws on data from field research supported by the Canadian International Food Security Research Fund, through the International Development Research Center/Global Affairs Canada. The review aims to introduce a comprehensive framework linking pulse food productivity, bioavailability of micronutrients from pulses through household level processing, nutrition education, and marketing, and their leverage on nutrition security. The development of the framework and subsequent discussion are based on key findings of selected analysis of research conducted in two large pulse producing regions of Ethiopia (SNNPR and Oromia). In order to simplify the understanding of the pathways, 20 case studies conducted by independent research teams are cited and reviewed in the results section, and these are presented under four headings: 1) Agronomic practices and soil management; 2) Pulse based food processing; 3) Nutrition education; and 4) Gender and livelihood. It is noted that effective application of the framework creates an enabling environment for agriculture to become more nutrition-sensitive to respond to the growing concerns of hidden hunger and malnutrition among Ethiopian households.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.507
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.369
Teacher spread0.310 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2016
Admission routes3
Has abstractyes

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