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Record W146328619

Effect of Adding Two Types of Sodium Acetate Compounds on Corn Silage Quality and Aerobic Stability

2008· article· en· W146328619 on OpenAlexaff
Xinhui Zhang, Zhang Yonggen, HE Ying-fei

Bibliographic record

VenueZhongguo nongye Kexue · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsScience North
Fundersnot available
KeywordsSilagePreservativeFood scienceChemistryLactic acidDry matterSodiumAnimal scienceBiologyBacteriaOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

【Objective】The experiment was aimed to study the effect of two silage fermentative inhibitors-sodium diacetate (SDA) and sodium dehydroacetate (SD) that normally are used as food preservatives on quality and aerobic stability of corn silage 【Method】In the experiment,whole newly-mowed corn plants as raw materials were ensiled after treatment with 0.4% SDA and 0.1% SD,respectively,and then were taken to compare with negative control (no additives used) and positive control (with acidizer added,3 ml·kg-1 LuproMix NC). 【Result】The results of the experiment showed that SD-treated silage had lower values in the quantities of molds and yeasts,dry matter loss (DML),and VBN/TN than both the negative control and the positive control significantly (P0.05). SDA-treated silage had the highest lactic acid and acetic acid concentrations,but its DML and VBN/TN were lower than the negative control,but not significant (P0.05). For aerobic stability,SD-treated silage were the greatest (213 h),and SDA-teated silage (147 h) were poorer than the positive control (LuproMix-treated,187 h),but better than the negative control (118 h). 【Conclusion】Thus,addition of SD or SDA did not affect the quality of corn silage but significantly improved aerobic stability. In terms of nutrient preservation and aerobic stability,SD took advantage over SDA or LuproMix NC. Moreover,SD with lower application rate and less cost could be used as a promising fermentative inhibitor of the silage.

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 score0.553
Threshold uncertainty score0.247

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 head0.045
GPT teacher head0.286
Teacher spread0.240 · 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
Published2008
Admission routes1
Has abstractyes

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