MétaCan
Menu
Back to cohort
Record W2087843074 · doi:10.2118/137211-pa

Surfactant Enhanced Biodegradation of Petroleum Hydrocarbons in Oil Refinery Tank Bottom Sludge

2010· article· en· W2087843074 on OpenAlexaff
Xiaoxi Zhang, Jianbing Li, Yuefei Huang, Ronald W. Thring

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsBiodegradationPetroleumHydrocarbonBioremediationOil refineryPollutantOil sludgeChemistryEnvironmental chemistryWaste managementTotal petroleum hydrocarbonPulp and paper industryPetroleum productEnvironmental scienceContaminationOrganic chemistry

Abstract

fetched live from OpenAlex

Summary Bioremediation has been recognized as an effective method to treat petroleum hydrocarbon pollutants. However, the biodegradation of crude oil-contaminated sludge could be a time-consuming and low-efficiency process. Among the reasons is that some petroleum hydrocarbons in the sludge are unavailable for micro-organisms? utilization. Surfactants have the potential to increase the bioavailability of such pollutants because of their capability to reduce the surface and interfacial tension and increase the solubility of hydrocarbons in water. In this study, the addition of two different chemical surfactants (Igepal CO-630 and Cedephos FA-600) were tested using a laboratory respirometer, and the effects of such surfactants on the biodegradation of total petroleum hydrocarbon (TPH) in the oil refinery sludge were investigated. Both surfactants have been found to be effective on improving microbial growth at low-concentration additions, while the concentration of 400 mg/kg has been found most effective for improving TPH (C10-C50) reduction after 14 days of biodegradation.

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 score0.441
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.195
Teacher spread0.189 · 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

Citations19
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Canadian Petroleum TechnologySame topicMicrobial bioremediation and biosurfactantsFrench-language works237,207