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Record W2160793801 · doi:10.1139/s06-057

Comparative study of PAH removal efficiency under absence of molecular oxygen: effect of electron acceptor and hydrodynamic conditions

2007· article· en· W2160793801 on OpenAlexvenueno aff
Alberto Uribe-Jongbloed, Paul L. Bishop

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesNational Institutes of Health
KeywordsSulfateDenitrificationChemistryDenitrifying bacteriaPyreneElectron acceptorPhenanthreneEnvironmental chemistryNaphthaleneShear (geology)Sulfate-reducing bacteriaBiodegradationPhotochemistryOrganic chemistryNitrogenMaterials scienceComposite material

Abstract

fetched live from OpenAlex

A series of experiments were made in order to compare the removal efficiency of a mixture of four PAHs (naphthalene, phenanthrene, pyrene, and benzo[a]pyrene), under different electron acceptor (NO 3 - , SO 4 -2 ) and hydrodynamic conditions (stagnation and high shear). In all cases naphthalene showed the highest removal efficiency (from 69% up to 100%) as compared with the other PAHs. The fastest rate was obtained for the denitrifying-high shear condition followed by denitrification-no shear, sulfate-reduction-high shear and the lowest for sulfate reduction-no shear. However, most of the perceived removal of the heavier PAHs could be due to aging. No lag time was observed for the denitrifying experiments, and the denitrification rate was the same regardless of the hydrodynamic condition. A lag time of 64 d was observed under conditions of sulfate reduction and high shear. Sulfate reduction did not commence under no shear conditions. No toxic effect was observed for the four PAH mixture under all the conditions tested. Key words: anaerobic systems, biodegradation, denitrification, hydrodynamic conditions, PAH, sulfate reduction.

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)0.001
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 score0.386
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.004
GPT teacher head0.224
Teacher spread0.220 · 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

Citations12
Published2007
Admission routes1
Has abstractyes

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