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Record W2334227149 · doi:10.1139/cjas-2015-0079

Effects of an exogenous enzyme-containing inoculant on fermentation characteristics of barley silage and on growth performance of feedlot steers

2016· article· en· W2334227149 on OpenAlexafffundvenue
W. Addah, J. Baah, Tim A. McAllister

Bibliographic record

VenueCanadian Journal of Animal Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsSilageMicrobial inoculantLactobacillus plantarumFermentationDry matterAnimal scienceForageFeedlotAgronomyHordeum vulgareBiologyChemistryLactic acidFood scienceInoculationHorticulturePoaceaeBacteria

Abstract

fetched live from OpenAlex

This study investigated the effects of an inoculant on silage fermentation, aerobic stability, and the growing and finishing performance of feedlot steers. Whole-crop barley (Hordeum vulgare L.) was chopped, wilted [350–400 g kg −1 dry matter (DM)], and ensiled without (Control) or with (Treated) a bacterial inoculant containing a mixture of Pediococcus pentosaceus, Lactobacillus plantarum, and Propionibacterium freudenreicheii (1.3 × 105 CFU g −1 forage), as well as enzymes applied to fresh forage ensiled in mini or Ag-Bag ® silos. Inoculation resulted in a pH decline (P < 0.05) from 6.0 on the day of ensiling to <4.0 after 3 d. In contrast, it required more than 20 d for the pH of the Control silage to fall below 4.0. Inoculant reduced (P < 0.05) the concentration of acetic acid and aerobic stability of silage, as evidenced by a higher (P < 0.05) temperature and pH in aerobically exposed silage. Although the inoculant accelerated pH decline during ensiling, it did not improve the growth performance (P < 0.05) or alter the carcass traits of steers. It is possible that a reduction in the aerobic stability of the inoculated silage may have contributed to this outcome.

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 score0.677
Threshold uncertainty score0.147

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 head0.014
GPT teacher head0.209
Teacher spread0.196 · 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

Citations17
Published2016
Admission routes3
Has abstractyes

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