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Record W1976908605 · doi:10.3390/ani4010082

Effect of Corn Dried Distiller Grains with Solubles (DDGS) in Dairy Cow Diets on Manure Bioenergy Production Potential

2014· article· en· W1976908605 on OpenAlexafffund
Daniel I. Massé, Guillaume Jarret, C. Benchaar, Noori M. Cata Saady

Bibliographic record

VenueAnimals · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaCanadian Dairy CommissionDairy Farmers of Canada
KeywordsDistillers grainsDry matterFood scienceChemistryNeutral Detergent FiberSlurryManureAnimal scienceBioenergyAgronomyBiofuelBiologyBiotechnology

Abstract

fetched live from OpenAlex

The main objective of this study was to obtain scientifically sound data on the bioenergy potential of dairy manures from cows fed different levels of corn dried distillers grains with solubles (DDGS). Three diets differing in corn DDGS content were formulated: 0% corn DDGS (DDGS0; control diet), 10% corn DDGS (DDGS10) and 30% corn DDGS (DDGS30). Bioenergy production was determined in psychrophilic (25 ± 1 °C) sequencing batch reactors (SBRs) fed 3 g COD L(-1)·day(-1) during a two-week feeding period followed by a two-week react period. Compared to the control diet, adding DDGS10 and DDGS30 to the dairy cow diet increased the daily amount of fat excreted in slurry by 29% and 70%, respectively. The addition of DDGS30 increased the cows' daily production of fresh feces and slurry by 15% and 11%, respectively. Furthermore, the incorporation of DDGS30 in the diet increased the daily amounts of dry matter (DM), volatile solids (VS), neutral detergent fiber (NDF), acid detergent fiber (ADF) and hemicellulose by 18%, 18%, 30%, 15% and 53%, respectively, compared to the control diet. While the addition of DDGS did not significantly affect the specific CH₄ production per kg VS compared to the control diet, DDGS30 increased the per cow daily CH₄ production by 14% compared to the control diet.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.483

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 head0.003
GPT teacher head0.199
Teacher spread0.197 · 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

Citations16
Published2014
Admission routes2
Has abstractyes

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