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Record W2929601377 · doi:10.30635/2415-0142.2019.05.1

Diversity, Utilization and Proximate Composition of Indigenous Leafy Vegetables Consumed in Malaysia

2019· article· en· W2929601377 on OpenAlexvenueno aff
Noorasmah Saupi, Ainul Asyira Saidin, Muta Ya, S. Noorasmah S. R. Sarbini

Bibliographic record

VenueJournal of Agriculture Food and Development · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLeafy vegetablesProximateIndigenousNutrientBiologySeasoningFood scienceLeafyGeographyBotanyRaw materialEcology

Abstract

fetched live from OpenAlex

The availability of indigenous leafy vegetables abundantly available in rural communities’ surroundings plays important role in its sustainability. A study was conducted at three native markets in Bintulu Division of Malaysia: Bintulu (N 3º10’29.5″ E 113º02’27.8″)Tatau (N 2º52’34.4″ E 112º51’09.6″) and Sebauh(N 3º06’38.6″ E 112º16’05.7″) to determine the plant species frequently used as leafy vegetables by the locals. The structured questionnaire was distributed to identify the species consumed and gather the information on species used for food consumption, vernacular name and utilization methods. Twenty species from 18 different families were identified with Euphorbiaceae dominated by three species. The locals used the indigenous leafy vegetables (ILV) in the preparation of fried vegetables, eaten raw, fermented and seasoning. The proximate composition conducted on 13 species of ILV revealed that these vegetables contains significantly high moisture (63.83 – 88.08%), fiber (9.20 – 27.39%) and carbohydrate (33.33 – 61.32%) contents whereas the fat (0.09 – 1.09%), proteins (0.25 – 1.65%) and energy (128.89 – 277.53 Kcal/100g) are low. The consumption of the ILV are recommended due to its potential to supply adequate intake of essential nutrients. Further study on the nutritional values of the ILV should be conducted to reveal many information on its nutritional values.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.194

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 head0.022
GPT teacher head0.241
Teacher spread0.219 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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