MétaCan
Menu
Back to cohort
Record W2616914211 · doi:10.3390/ijms18051105

The Occurrence, Biosynthesis, and Molecular Structure of Proanthocyanidins and Their Effects on Legume Forage Protein Precipitation, Digestion and Absorption in the Ruminant Digestive Tract

2017· review· en· W2616914211 on OpenAlexaff
Arjan Jonker, Peiqiang Yu

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2017
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLotus corniculatusProanthocyanidinRuminantLegumeTrefoilBiologyDigestion (alchemy)ForageMedicago sativaMonogastricRumenCondensed tanninTanninFood scienceAnimal nutritionBiochemistryAgronomyChemistryPolyphenolPastureFermentationAntioxidant

Abstract

fetched live from OpenAlex

Forages grown in temperate regions, such as alfalfa (Medicago sativa L.) and white clover (Trefolium repens L.), typically have a high nutritional value when fed to ruminants. Their high protein content and degradation rate result, however, in poor utilization of protein from the forage resulting in excessive excretion of nitrogen into the environment by the animal. Proanthocyanindins (also known as condensed tannins) found in some forage legumes such as birdsfoot trefoil (Lotus corniculatus L.), bind to dietary protein and can improve protein utilization in the animal. This review will focus on (1) the occurrence of proanthocyanidins; (2) biosynthesis and structure of proanthocyanidins; (3) effects of proanthocyanidins on protein metabolism; (4) protein precipitating capacity of proanthocyanidins and their effects on true intestinal protein adsorption by ruminants; and (5) effect on animal health, animal performance and environmental emissions.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.304
Teacher spread0.275 · 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

Citations62
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Molecular SciencesSame topicRuminant Nutrition and Digestive PhysiologyFrench-language works237,207