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Record W3184068104 · doi:10.1080/19490976.2021.1926842

Resistant starch, microbiome, and precision modulation

2021· review· en· W3184068104 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGut Microbes · 2021
Typereview
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Ottawa
FundersW. Garfield Weston FoundationOntario Ministry of Economic Development and Innovation
KeywordsMicrobiomeResistant starchBiologyGut microbiomeStarchPhenotypeBiotechnologyComputational biologyBioinformaticsFood scienceGeneticsGene

Abstract

fetched live from OpenAlex

Resistant starch, microbiome, and precision modulation. Mounting evidence has positioned the gut microbiome as a nexus of health. Modulating its phylogenetic composition and function has become an attractive therapeutic prospect. Resistant starches (granular amylase-resistant α-glycans) are available as physicochemically and morphologically distinguishable products. Attempts to leverage resistant starch as microbiome-modifying interventions in clinical studies have yielded remarkable inter-individual variation. Consequently, their utility as a potential therapy likely depends predominantly on the selected resistant starch and the subject's baseline microbiome. The purpose of this review is to detail i) the heterogeneity of resistant starches, ii) how resistant starch is sequentially degraded and fermented by specialized gut microbes, and iii) how resistant starch interventions yield variable effects on the gut microbiome.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.047
GPT teacher head0.313
Teacher spread0.266 · 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