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Record W2091307535 · doi:10.1111/ijfs.12246

Starch fraction profiles of milled, nonparboiled rice varieties from <scp>N</scp>igeria

2013· article· en· W2091307535 on OpenAlexaff
Amaka Odenigbo, Michael Ngadi, John Manful, Nahemiah Danbaba

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2013
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsMcGill University
FundersNational Cancer Research Institute
KeywordsStarchResistant starchFood scienceDigestion (alchemy)BiologyChemistryChromatography

Abstract

fetched live from OpenAlex

Summary This study determined the levels of nutritionally important starch fractions in selected milled, nonparboiled rice cultivated in Nigeria. Five improved varieties (FARO 52, FARO 57, FARO 44, FARO 60 and FARO 61) and four local varieties (Kwandala, Yardass, Jeep and Jamila) were evaluated. There were significant differences in starch fractions among varieties. Resistant starch (RS) ranged between 1.43% and 3.13%. Rapidly digestible starch (RDS) was lowest in Jamila (27.70%) and highest in FARO 61 (39.26%). Generally, the local varieties had significant higher RS (2.71%) with a lower RDS (32.82%) compared with improved varieties (RS; 1.88% and RDS; 36.07%). RS was inversely related to RDS and starch digestion index (SDI). The SDI had a highly significant positive correlation with RDS (r = 0.879, P < 0.01). These results highlight the need for further work in the identification of milled, nonparboiled rice varieties with less rapid digestion for its associated health benefits to consumers.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.015
GPT teacher head0.268
Teacher spread0.253 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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