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Record W2070851743 · doi:10.1139/s08-033

Environmental innocuousness of the activation of a microbial consortium from creosote-contaminated soil in a slurry bioreactor

2008· article· en· W2070851743 on OpenAlexafffundvenue
Dominic D’Amours, Réjean Samson, Louise Deschênes

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsCreosoteBioaugmentationBiodegradationPhenanthreneEnvironmental chemistrySoil contaminationMicrocosmBiostimulationBioremediationContaminationChemistryTemperature gradient gel electrophoresisEnvironmental scienceSoil waterEcologyBiologySoil science

Abstract

fetched live from OpenAlex

A laboratory study was conducted to assess the environmental innocuousness of an inoculum production (soil activation) to be used in the bioaugmentation of creosote-contaminated soils. The activation was carried out through multiple creosote addition. The increased biodegradation and mineralisation rates of the polycyclic aromatic hydrocarbons (PAHs) indicate that the metabolic capabilities of the microbial consortium were enhanced during activation. The PAH mass balance shows a removal of 96.4%. Biodegradation contributed to the removal of the 3-ring (37%–67%), 4-ring (50%–73%), and 5&6-ring (1%–53%) PAHs. Abiotic losses were significant in 2 and 3-ring PAHs. An increase in phenanthrene degraders from 2.1 × 10 5 to 1.0 × 10 8 CFU mL –1 was observed. Denaturing gradient gel electrophoresis analyses indicate the occurrence of a selection of microbial strains, but biodiversity is probably unaffected by creosote addition. Results suggest that soil activation may have the potential to ensure the environmental safety of the bioaugmentation of creosote-contaminated soil.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.354

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 studies0.0000.001
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 head0.005
GPT teacher head0.161
Teacher spread0.156 · 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

Citations2
Published2008
Admission routes3
Has abstractyes

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