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Record W2609374317 · doi:10.1038/s41598-017-01269-3

Repeated inoculation of cattle rumen with bison rumen contents alters the rumen microbiome and improves nitrogen digestibility in cattle

2017· article· en· W2609374317 on OpenAlexafffund
Gabriel O Ribeiro, Daniela Batista Oss, Zhixiong He, Robert J. Gruninger, Chijioke Elekwachi, Robert J. Forster, Wenzhu Yang, K. A. Beauchemin, Tim A. McAllister

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersElanco Animal HealthAgriculture and Agri-Food CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsRumenRuminantBiologyStrawMicrobiomeAnimal scienceFermentationAgronomyBeef cattleAnimal feedFood sciencePasture

Abstract

fetched live from OpenAlex

Future growth in demand for meat and milk, and the socioeconomic and environmental challenges that farmers face, represent a "grand challenge for humanity". Improving the digestibility of crop residues such as straw could enhance the sustainability of ruminant production systems. Here, we investigated if transfer of rumen contents from bison to cattle could alter the rumen microbiome and enhance total tract digestibility of a barley straw-based diet. Beef heifers were adapted to the diet for 28 days prior to the experiment. After 46 days, ~70 percent of rumen contents were removed from each heifer and replaced with mixed rumen contents collected immediately after slaughter from 32 bison. This procedure was repeated 14 days later. Intake, chewing activity, total tract digestibility, ruminal passage rate, ruminal fermentation, and the bacterial and protozoal communities were examined before the first and after the second transfer. Overall, inoculation with bison rumen contents successfully altered the cattle rumen microbiome and metabolism, and increased protein digestibility and nitrogen retention, but did not alter fiber digestibility.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.021
GPT teacher head0.238
Teacher spread0.217 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations85
Published2017
Admission routes2
Has abstractyes

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