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Record W2947226170 · doi:10.1051/e3sconf/20199601004

A review- bioremediation of oil sludge contaminated soil

2019· article· en· W2947226170 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

VenueE3S Web of Conferences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBioremediationEnvironmental scienceWaste managementContaminationPetroleumOil sludgeOil refineryPetroleum industrySoil contaminationSewage sludgePetroleum productEnvironmental engineeringSewageChemistrySoil waterEngineeringEcology

Abstract

fetched live from OpenAlex

Petroleum oil as a vast source of energy widely used in the whole world in several sectors especially in industry and transportation. The leakage or contamination of oil from pipeline, tank, and industry as a form of oil sludge with soil can produce major environmental and health hazard. Bioremediation is one of the most economical and environmentally safe technology to prevent this contamination though it takes longer time. This paper reviews the basic processes involved in bioremediation, types and the factors affecting it. This study includes some previously adopted different bioremediation methods varies with different process material such as refinery treatment sludge, sewage sludge, microbial organism, bulking agents and different chemical additives. The comparison of these methods is presented in respect of the removal efficiency of an entire process as well as the TPH (Total Petroleum Hydrocarbon), aliphatic, aromatic, resins, asphaltene fraction of oil sludge within the different period of time.

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 categoriesInsufficient payload (model declined to judge)
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 score0.124
Threshold uncertainty score0.990

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)0.0110.001

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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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