MétaCan
Menu
Back to cohort
Record W3012515161 · doi:10.1080/09064702.2020.1737215

Propionic acid bacteria enhance ruminal feed degradation and reduce methane production <i>in vitro</i>

2020· article· en· W3012515161 on OpenAlexaff
Jikun Chen, O. M. Harstad, Tim A. McAllister, Peter Dörsch, Helge Holo

Bibliographic record

VenueActa Agriculturae Scandinavica Section A – Animal Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersNorges Forskningsråd
KeywordsRumenFood scienceBacteriaSubstrate (aquarium)ChemistryFatty acidSilageMethaneDegradation (telecommunications)Lactic acidPropionibacteriumAnimal scienceBiochemistryBiologyFermentationOrganic chemistryEcology

Abstract

fetched live from OpenAlex

Thirty-one strains of propionic acid bacteria were screened for their effects on methane production and volatile fatty acid concentrations using in vitro assays of rumen fluid from Norwegian dairy cows and a grass silage–concentrate mixture as substrate. Nine of 31 strains were further analysed for effects on substrate degradation. Propionic acid bacteria led to reductions of up to 20% in methane production. Seven strains stimulated volatile fatty acid production, and in their presence in vitro substrate degradation tended to increase (P < .10). Most consistent results were found with Propionibacterium thoenii T159, which reduced methane production by 20% and caused 8% and 21% overall increases in substrate degradation and total volatile acid production, respectively (P < .05). Concomitant beneficial effects of a reduction in methane emissions and an increase in feed degradation suggest that this strain may be a promising tool for improving the productive performance of dairy cows.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.020
GPT teacher head0.242
Teacher spread0.222 · 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 designBench or experimental
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

Citations27
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueActa Agriculturae Scandinavica Section A – Animal ScienceSame topicRuminant Nutrition and Digestive PhysiologyFrench-language works237,207