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Record W3076639633 · doi:10.1139/cjas-2019-0187

In vitro ruminal fermentation and methane inhibitory effect of three species of microalgae

2020· article· en· W3076639633 on OpenAlexvenueno aff
Ali Kiani, Christina Wolf, Katrin Giller, Lukas Eggerschwiler, Michael Kreuzer, Angela Schwarm

Bibliographic record

VenueCanadian Journal of Animal Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
FundersAgroscope
KeywordsIsovaleratePropionateMethanogenesisValerateAlgaeFermentationFood sciencePhaeodactylum tricornutumChemistryHeterotrophChlorophytaDry matterBiologyAnimal scienceBotanyBiochemistryMethaneButyrateBacteriaOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, in vitro ruminal fermentation, anti-methanogenesis, and ammonia formation of two autotrophic algae [Nannochloropsis gaditana (NG), Phaeodactylum tricornutum (PT)], and one heterotrophic alga [Schizochytrium sp. (SS)] were investigated. The experimental diets consisted of a hay-concentrate basal diet (BD; 200 mg dry matter) supplemented with (1) no algae (just BD), (2) 40 mg of dried NG (BD + NG), (3) 40 mg of dried PT (BD + PT), and (4) 14 mg of dried SS. In total, 48 samples (four algal treatments × two replicates × three runs × two cows) were incubated for 24 h using the Hohenheim gas test method. All three algae decreased (P < 0.05) the production of short-chain fatty acids and protozoal abundance (both adjusted in amount to BD) as compared with BD. Ammonia formation of BD + NG and BD + PT was 1.2- and 1.1-fold of values in BD, respectively. The BD + NG diet enhanced the proportions of isobutyrate, valerate, and isovalerate at cost of acetate proportion of total short-chain fatty acids, whereas the BD + PT diet promoted the proportions of propionate and valerate at cost of acetate. None of the microalgae affected in vitro methane formation. In conclusion, these algae showed a very poor fermentability and no anti-methanogenic effect in vitro.

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 score0.507
Threshold uncertainty score0.199

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 studies0.0000.001
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 head0.023
GPT teacher head0.232
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 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

Citations15
Published2020
Admission routes1
Has abstractyes

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