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Influence of distiller's grains and condensed tannins in the diet of feedlot cattle on biohydrogen production from cattle manure

2013· article· en· W1978341686 on OpenAlexafffund
Brandon H. Gilroyed, Chunli Li, Tim Reuter, K. A. Beauchemin, Xiying Hao, Tim A. McAllister

Bibliographic record

VenueInternational Journal of Hydrogen Energy · 2013
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsAgriculture Food and Rural DevelopmentAgriculture and Agri-Food CanadaUniversity of Guelph
FundersNatural Resources CanadaAgriculture and Agri-Food Canada
KeywordsBiohydrogenManureDistillers grainsDry matterChemistryFeedlotSlurryAmmoniaFood scienceAnimal scienceTanninAgronomyHydrogenHydrogen productionBiologyMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

Biohydrogen production from the manure of cattle fed diets containing corn dried distiller's grains with solubles (DDGS) diets was assessed. Four types of manure were obtained from cattle fed four diets (DDG, %of dietary dry matter): 0 (CK), 20 (DG20), 40 (DG40) and 40 plus 2.5% of dietary dry matter as condensed tannins (DG40CT) and evaluated for biohydrogen production using dark fermentation. Each treatment was evaluated in quadruplicate using 2 L continuously stirred biodigesters operating at 55 °C in batch culture with an organic loading rate of 20 g L−1 volatile solids and a total operation time of 4 d. Gas samples were taken daily to determine hydrogen production, and slurry samples were analyzed daily for volatile fatty acid concentration, total ammonia nitrogen, volatile solids degradation, and soluble ion concentration. The DG0 and DG40 treatments demonstrated the greatest hydrogen production, with DG40CT producing the least (P < 0.001). The inclusion of tannins in the diet of cattle had a negative effect on biohydrogen production from cattle manure, and thus the economic feasibility of using manure as a substrate in anaerobic digestion could be negatively impacted by the inclusion of tannins in the 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.274

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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

Citations18
Published2013
Admission routes2
Has abstractyes

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