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Record W2152483471 · doi:10.3168/jds.2014-8768

Conservation, fiber digestibility, and nutritive value of corn harvested at 2 cutting heights and ensiled with fibrolytic enzymes, either alone or with a ferulic acid esterase-producing inoculant

2014· article· en· W2152483471 on OpenAlexafffund
Joseph P. Lynch, J. Baah, K. A. Beauchemin

Bibliographic record

VenueJournal of Dairy Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaAlberta Livestock and Meat Agency
KeywordsSilageNeutral Detergent FiberMicrobial inoculantFermentationChemistryDry matterFood scienceFerulic acidCellulaseLactic acidAgronomyAnimal scienceBiologyBiochemistryEnzymeHorticultureInoculationBacteria

Abstract

fetched live from OpenAlex

The aim of this study was to determine the effects of the use of a fibrolytic enzyme product, applied at ensiling either alone or in combination with a ferulic acid esterase-producing bacterial additive, on the chemical composition, conservation characteristics, and in vitro degradability of corn silage harvested at either conventional or high cutting height. Triplicate samples of corn were harvested to leave stubble of either a conventional (15cm; NC) or high (45cm; HC) height above ground. Sub-samples of chopped herbage were ensiled untreated or with a fibrolytic enzyme product containing xylanases and cellulases applied either alone (ENZ) or in combination with a ferulic acid esterase-producing silage inoculant (ENZ+FAEI). The fibrolytic enzyme treatment was applied at 2mL of enzyme product/kg of herbage dry matter (DM), and the inoculant was applied at 1.3×10(5) cfu/g of fresh herbage. Samples were packed into laboratory-scale silos, stored for 7, 28, or 70 d, and analyzed for fermentation characteristics, and samples ensiled for 70 d were also analyzed for DM losses, chemical composition, and in vitro ruminal degradability. After 70 d of ensiling, the fermentation characteristics of corn silages were generally unaffected by cutting height, whereas the neutral detergent fiber, acid detergent fiber, and ash concentrations were lower and the starch concentration greater for silages made with crops harvested at HC compared with NC. After 70 d of ensiling, the acetic acid, ethanol concentrations, and the number of yeasts were greater, and the pH and neutral detergent fiber concentrations were lower, in silages produced using ENZ or ENZ+FAEI than the untreated silages, whereas ENZ+FAEI silages also incurred higher DM losses. No effect of additive treatment was observed on in vitro degradability indices after 48h ruminal incubation. The use of a fibrolytic enzyme product, either alone or in combination with a ferulic acid esterase-producing inoculant, at ensiling did not improve corn silage fermentation or its nutritive value and resulted in some negative effects on these parameters. The effects of using a fibrolytic enzyme product at ensiling, either alone or in combination with a ferulic acid esterase-producing inoculant, did not differ between corn harvested at either NC or HC. Silage made from HC had a greater starch content and lower fiber content than NC silage, whereas cutting height did not affect the in vitro digestibility indices.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.017
GPT teacher head0.225
Teacher spread0.208 · 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

Citations68
Published2014
Admission routes2
Has abstractyes

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