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Record W2044620264 · doi:10.4141/a01-078

In vitro ruminal methane suppression by lauric acid as influenced by dietary calcium

2002· article· en· W2044620264 on OpenAlexvenueno aff
Andrea Machmüller, C.R. Soliva, Michael Kreuzer

Bibliographic record

VenueCanadian Journal of Animal Science · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsLauric acidFermentationFood scienceChemistryRumenFatty acidDry matterOrganic matterCalciumButyric acidAnimal scienceBiochemistryBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of Ca supplementation on the methane-suppressing effect of lauric acid was investigated in an experiment based on a 2 × 2-factorial arrangement using the in vitro system Rusitec. Additional Ca was supplemented at 1 g kg -1 diet in the form of compounds of relatively low solubility in rumen fluid. Lauric acid (C 12:0 ), the predominant effective medium-chain fatty acid in coconut oil, was added at a level of 50 g kg -1 . Adding C 12:0 did not affect bacterial count, but eliminated ciliate protozoa from fermenters. Ammonia concentration in fermentation mixture declined and volatile fatty acid pattern changed with C 12:0 . The apparent degradation rate of total organic matter was not altered by C 12:0 , but fiber fermentation was depressed (P < 0.001). Effects of Ca on microbial counts and fermentation characteristics remained low. Without additional Ca, C 12:0 reduced the average daily methane release (mmol g -1 organic matter degraded) by 76%. In comparison, C 12:0 only reduced methane production by 47% when additional Ca was included in the diet (interaction of C 12:0 and Ca, P < 0.05). The present results suggest that the dietary content of soap-forming Ca has to be kept low in order to achieve a high methane-suppressing effect of lauric acid. Key words: Methane, lauric acid, lipids, calcium, Rusitec, ruminants

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 categoriesInsufficient payload (model declined to judge)
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 score0.194
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.026
GPT teacher head0.251
Teacher spread0.225 · 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.

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

Citations42
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Animal ScienceSame topicRuminant Nutrition and Digestive PhysiologyFrench-language works237,207