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Record W2758008135 · doi:10.1111/asj.12915

Effect of changing forage on the dynamic variation in rumen fermentation in sheep

2017· article· en· W2758008135 on OpenAlexaff
Xiao Xie, Jia‐kun Wang, Leluo Guan, Jianxin Liu

Bibliographic record

VenueAnimal Science Journal · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsForageRumenFermentationAnimal scienceVariation (astronomy)AgronomyBiologyChemistryFood sciencePhysics

Abstract

fetched live from OpenAlex

To better understand rumen adaptation during dietary transitions between high- and low-quality forages, 10 rumen-cannulated Hu sheep were randomly allocated to two dietary treatments (five sheep each) with the same concentrate-to-forage ratio and concentration mixture, but different forage sequences: (i) alfalfa hay (AH) to corn stover (CS) and back to AH; and (ii) CS to AH and back to CS. A significant decrease in the rumen microbial protein concentration was observed on day 6 after dietary transition whether the transition was from AH to CS or from CS to AH, and this was accompanied by an increase in the ammonia nitrogen concentration as well as a decrease in the total volatile fatty acids concentration and pH. However, after transitioning back to the original forage, the rumen fermentation parameters returned to their initial levels within 2 weeks. Our findings suggest that abrupt substitutions of forages with large nutrient differences could influence rumen function to some extent, but recovery can occur within 2 weeks without detrimental effects. Furthermore, we speculate that the variation of fermentation in the first 6 days may indicate an important rumen transition stage that requires further study.

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)0.002
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 score0.965
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.016
GPT teacher head0.286
Teacher spread0.270 · 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

Citations17
Published2017
Admission routes1
Has abstractyes

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