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Record W1588623665 · doi:10.2166/wst.2000.0520

Immobilization strategies for bioaugmentation of anaerobic reactors treating phenolic compounds

2000· article· en· W1588623665 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWater Science & Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsInstitut National de la Recherche ScientifiqueNational Research Council CanadaBiotechnology Research InstituteUniversité de Sherbrooke
Fundersnot available
KeywordsBioaugmentationPhenolChemistryAnaerobic exerciseBiodegradationHydraulic retention timePulp and paper industryBioreactorDegradation (telecommunications)Anaerobic digestionChromatographyBacteriaWaste managementMethaneWastewaterOrganic chemistryMicroorganismBiology

Abstract

fetched live from OpenAlex

Degradation of phenol, ortho- and para-cresol was investigated in upflow anaerobic sludge bed (UASB) reactors bioaugmented with a methanogenic enrichment consortium able to degrade a mixture of phenolic compounds, in comparison to a reactor, which was inoculated only with anaerobic granules: 1) natural attachment of free cells to the granules, and 2) encapsulation within alginate beads. The increase of the percentage of enrichment from 2 to 5% improved considerably the startup of the reactors. Going from 5 to 10% had no effect on the removal of the phenolic compounds. Following a period of continuous operation at a hydraulic retention time of 3 days, the bioaugmented reactors showed specific activities on phenol, ortho- and para-cresol, at least twofold higher than those of the control reactor. This increase was attributed to the immobilization of phenol, ortho- and para-cresol-degrading bacteria on the granules.

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.

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 score0.151

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 head0.010
GPT teacher head0.232
Teacher spread0.223 · 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