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Record W1484453295 · doi:10.3389/fmicb.2015.00597

Anaerobic digestion of the microalga Spirulina at extreme alkaline conditions: biogas production, metagenome, and metatranscriptome

2015· article· en· W1484453295 on OpenAlexaff
Vímac Nolla-Ardèvol, Marc Strous, Halina E. Tegetmeyer

Bibliographic record

VenueFrontiers in Microbiology · 2015
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of Calgary
FundersUniversität BielefeldDeutsche Forschungsgemeinschaft
KeywordsBiogasAnaerobic digestionSpirulina (dietary supplement)BioenergyMetagenomicsMethaneFood scienceHydraulic retention timePulp and paper industryChemistryAnaerobic exerciseBiologyWastewaterBiofuelEnvironmental scienceBiotechnologyEnvironmental engineeringBiochemistryEcology

Abstract

fetched live from OpenAlex

A haloalkaline anaerobic microbial community obtained from soda lake sediments was used to inoculate anaerobic reactors for the production of methane rich biogas. The microalga Spirulina was successfully digested by the haloalkaline microbial consortium at alkaline conditions (pH 10, 2.0 M Na(+)). Continuous biogas production was observed and the obtained biogas was rich in methane, up to 96%. Alkaline medium acted as a CO2 scrubber which resulted in low amounts of CO2 and no traces of H2S in the produced biogas. A hydraulic retention time (HRT) of 15 days and 0.25 g Spirulina L(-1) day(-1) organic loading rate (OLR) were identified as the optimal operational parameters. Metagenomic and metatranscriptomic analysis showed that the hydrolysis of the supplied substrate was mainly carried out by Bacteroidetes of the "ML635J-40 aquatic group" while the hydrogenotrophic pathway was the main producer of methane in a methanogenic community dominated by Methanocalculus.

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 score0.453
Threshold uncertainty score0.583

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 head0.018
GPT teacher head0.205
Teacher spread0.186 · 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

Citations81
Published2015
Admission routes1
Has abstractyes

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