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Prospect of Fe non-heme on coffee flour made from solid coffee waste: Mini review

2019· article· en· W2954056338 on OpenAlexaboutno aff
Roy Hendroko Setyobudi, Lili Zalizar, Satriyo Krido Wahono, Wahyu Widodo, Ahmad Wahyudi, Maizirwan Mel, Bayu Prabowo, Yahya Jani, Yogo Adhi Nugroho, Tony Liwang, A Zaebudin

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
Fundersnot available
KeywordsFood scienceChemistryHuskPulp (tooth)BiotechnologyAgricultural scienceBiologyBotanyMedicine

Abstract

fetched live from OpenAlex

Abstract Coffee flour (CF) from coffee pulp or husk, solid waste of coffee processing have launched in Canada since 2015. This product is claimed as certified of gluten-free, vegan, kosher, paleo, and non-GMO. Coffe flour is stated to contain three times Fe content than fresh spinach (Spinacia oleracea L.). Several receipts of cookies, donuts, and cakes using CF has been introduced as wheat flour substitution. However, the scientific publication of CF impact for health does not appear until August 2018 yet. A review has been carried out using data on Google with a maximum publication age of 15 yr. This Fe non-heme prospect is allegedly unable to be absorbed optimally by the organism. Coffee pulp and husk contain an inhibitor, such as caffeine, polyphenol, calcium, dietary fiber, manganese, magnesium, and zinc; which detain Fe absorption. On the other hand, the promoter/enhancer of Fe absorption such as vitamin C, vitamin A, and amino acid was decreased in CF processing. Several types of research have to be conducted to tackle this problem in Faculty of Medicine and Faculty of Agriculture and Animal Husbandry University Muhammadyah of Malang, Indonesia.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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.

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

Citations31
Published2019
Admission routes1
Has abstractyes

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