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Record W2500778405 · doi:10.5772/64091

Prebiotics: Metabolism and Symbiotic Synergy with Probiotics in Promoting Health

2016· book-chapter· en· W2500778405 on OpenAlexaboutno aff
Nditange Shigwedha, Penny Hiwilepo-van Hal, Li Jia, Liubov Sichel, Shuang Zhang

Bibliographic record

VenueInTech eBooks · 2016
Typebook-chapter
Languageen
FieldNursing
TopicMicrobial Metabolites in Food Biotechnology
Canadian institutionsnot available
Fundersnot available
KeywordsDietary fiberFood scienceProbioticHealth benefitsBiologyPrebioticBiotechnologyMedicineBacteriaTraditional medicine

Abstract

fetched live from OpenAlex

Prebiotics are non‐digestible food ingredients that beneficially affect the host by selectively stimulating the growth and activity of probiotic bacteria in the colon. All dietary prebiotics and/or dietary fiber provide the physiological and beneficial effects and, therefore, are considered as essential nutrients. According to the Codex Alimentarius and the Canadian Bureau of Nutritional Sciences, dietary fiber consists of carbohydrates with a degree of polymerization (DP) of three or more that naturally occur in foods of plant origin and that are not digested and absorbed by the small intestine. The same definition goes well along with the term dietary prebiotics. Food and Drug Administration (FDA)|Institute of Medicine (IOM) states that dietary fiber only comes from plant foods and anything else is regarded as “added fiber” or “novel fiber.” Dietary fiber and/dietary prebiotics can be industrially produced for a broad range of food applications. They can also be processed into capsules for the purpose of microencapsulating probiotics. In this chapter, the most recognized physiological and/or beneficial effects of the prebiotics are clarified. New evidence on the concentrations of the short‐chain fatty acids (SCFAs) and their metabolic relationship with better health or disease prevention in the host is provided.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.008

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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2016
Admission routes1
Has abstractyes

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