MétaCan
Menu
Back to cohort
Record W6959899565 · doi:10.13023/44c4-qt92

Effects of Incorporation of Sainfoin (Onobrychis viciafolia Scop.) with Cool Season Grasses on in vitro Digestibility and CH4 Emission

2024· other· en· W6959899565 on OpenAlexaff

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2024
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsRumenHayDry matterForagePerennial plantFermentationGrazingNeutral Detergent FiberNitrogenFodder

Abstract

fetched live from OpenAlex

Sainfoin (Onobrychis viciafolia Scop.) is an important non-bloating perennial leguminous forage. The tannins in sainfoin alter protein metabolism in the rumen and have been implicated in altering both nitrous oxide and methane emissions. However, the effect of sainfoin when mixed with cool-season forages is unknown. Therefore, in this study, we evaluated the in-vitro fermentation of sainfoin hay mixed with two other perennial cool-season forages, meadow bromegrass and orchardgrass at five ratios (0:100, 25:75, 50:50, 75:50, and 100:0). Data on dry matter disappearance (DMD), neutral detergent fiber disappearance (NDFD), gas production (GP) methane (CH4) emissions, and ammonia production were collected 48 h post incubation. Ruminal fluid was sourced from three heifers fed with forage hay. Incubations were conducted with and without PEG (polyethylene glycol) as PEG negates the biological activity of tannins. Sainfoin had a higher nutritive value than the other two grass species as evidenced by the higher proportion of total nitrogen and lower proportion of ADF and NDF. The change in DMD, ammonia-N, NDFD, GP, and CH4 emissions between sainfoin and grass only hay were 3.1, 9.2, -36.8, -1.76, and -1.2% respectively with the intermediate results for the mixture. The inclusion of sainfoin with cool-season grasses has positive effects on ruminal fermentation and lowered in vitro methane emissions as compared to grass alone

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueUKnowledge (University of Kentucky)Same topicWheat and Barley Genetics and PathologyFrench-language works237,207