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Record W2043253843 · doi:10.1080/03670240903308604

Potential Contribution of Mangoes to Reduction of Vitamin A Deficiency in Kenya

2009· article· en· W2043253843 on OpenAlexfundno aff
Penina Muoki, Anselimo Makokha, Christine A. Onyango, Nelson K. O. Ojijo

Bibliographic record

VenueEcology of Food and Nutrition · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsPostharvestRipenessVitamin A deficiencyVitaminCaroteneFood scienceRetinolHorticultureCarotenoidMedicineBiologyToxicologyRipeningBiochemistry

Abstract

fetched live from OpenAlex

The β-carotene content of fresh and dried mangoes commonly consumed in Kenya was evaluated and converted to retinol equivalent (RE). Mango fruits of varieties Ngowe, Apple, and Tommy Atkins were harvested at mature green, partially ripe, and ripe stages and their β-carotene content analyzed. The stability of β-carotene in sun dried mangoes was also studied over 6 months under usual marketing conditions used in Kenya. The effect of using simple pretreatment methods prior to drying of mango slices on retention of β-carotene was as well evaluated. In amounts acceptable to children and women, fresh and dried mangoes can supply 50% or more of the daily required retinol equivalent for children and women. Stage of ripeness, variety, postharvest holding temperature, method of drying, and storage time of dried mango slices affected β-carotene content and consequently vitamin A value of the fruits. Apple variety grown in Machakos had the highest β-carotene. It exceeded the daily RE requirements by 11.8% and 21.5% for women and children respectively. Fresh or dried mangoes are a significant provitamin A source and should be included in food-based approaches aiming to reduce vitamin A deficiency.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.074

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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 designBench or experimental
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

Citations26
Published2009
Admission routes1
Has abstractyes

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