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Record W4296617551 · doi:10.1093/jas/skac247.126

140 Redefining gut Barrier Function for Beef and Dairy Cattle

2022· article· en· W4296617551 on OpenAlexaff
G.B. Penner

Bibliographic record

VenueJournal of Animal Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEx vivoIntestinal permeabilityIn vivoBarrier functionPermeability (electromagnetism)BiologyChemistryCell biologyBiotechnologyImmunologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Of the many roles that the gastrointestinal tract (GIT) must facilitate, coordinated regulation of molecule movement across the gastrointestinal is key. This coordinated regulation allows for nutrient absorption while limiting permeation of non-desired molecules and is referred to as barrier function. Barrier function of the GIT includes intrinsic, extrinsic, and immunological factors, but generally permeability assessments are conducted to evaluate the whole-animal response and most studies focus on the extrinsic components of GIT barrier function. Common approaches to evaluate GIT permeability include the use of inert external markers varying in size and their measurement in blood or urine. Methodology exists for ex vivo, in vitro, and in vivo assessment; however, only in ex vivo and in vitro methods currently allow regionally specific information. Moreover, ex vivo studies have been essential to further elucidate factors that affect permeability such as osmolality, pH, and bioactive components such as histamine and lipopolysaccharides. For ruminants, extensive microbial degradation challenges the use of fermentable substrates as markers when assessing permeability and there is interest to compartmentalize permeability by region of the GIT. Recent research using dual markers to allow for total GIT and post-ruminal permeability have highlighted that regional permeability responses may be affected by production system, environmental conditions, and dietary characteristics. Future research is needed to improve the understanding of factors that affect GIT permeability, the regions involved, and strategies to support regulation of permeability.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.255
Teacher spread0.224 · 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 designTheoretical or conceptual
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

Citations0
Published2022
Admission routes1
Has abstractyes

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