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Record W1984164673 · doi:10.1021/bp050304v

Influence of C18 Long Chain Fatty Acids on Hydrogen Metabolism

2006· article· en· W1984164673 on OpenAlexafffund
J. Templer, Jerald A. Lalman, Pius M. Ndegwa

Bibliographic record

VenueBiotechnology Progress · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMetabolismChemistryBiochemistryLong chain

Abstract

fetched live from OpenAlex

During anaerobic treatment, several microorganisms mediate a series of reactions to convert reduced compounds (electron donors) into methane. Inhibitors such as long chain fatty acids (LCFAs) can affect several anaerobic microbial populations and decrease the treatment efficiency. The effects of three C18 LCFAs on hydrogenotrophic methanogens in a flocculated mixed anaerobic culture were assessed in this study. The reaction half-life and the hydrogen versus time profiles were used to characterize the inhibition process. The half-life values and profiles were similar for controls and cultures exposed to LCFAs for 1 h. The hydrogen inhibition was a function of the exposure time and the LCFA concentration except for cultures exposed to stearic acid (SA). A statistical analysis of the reaction half-life for cultures incubated with 1,500 and 2,000 mg L(-1) LCFAs for 48 h, revealed the following inhibition trend: linoleic acid (LA) > oleic acid (OA) > SA. After 48 h of exposure, no clear inhibition trend was observed for cultures inoculated with LCFA mixtures; however, at levels of 1,500 and 2,000 mg L(-1), the reaction half-life values were less than that observed for cultures fed with only LA. Based on the reaction half-life data, all of the LCFAs except SA at threshold levels of approximately 1,500 mg L(-1) inhibited hydrogen metabolism. The greatest inhibition and, hence, the largest amount of accumulated hydrogen was observed in cultures fed with 2,000 mg L(-1) LA and incubated for 48 h.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.706

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 integrity0.0010.000
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 head0.005
GPT teacher head0.227
Teacher spread0.222 · 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

Citations25
Published2006
Admission routes2
Has abstractyes

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