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Record W2940951010 · doi:10.1680/jenes.19.00002

Study of microbial combination and nutrients in remediation of petroleum-contaminated soil

2019· article· en· W2940951010 on OpenAlexvenueno aff
Thu Ra, Yaling Zhao, Maosheng Zheng

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsnot available
FundersMinistry of Science and Technology of the People's Republic of China
KeywordsNutrientBioremediationEnvironmental remediationPetroleumSoil contaminationNutrient agarEnvironmental chemistryContaminationDegradation (telecommunications)BiodegradationBacteriaMicroorganismEnvironmental scienceChemistryFood scienceBiologyAgarEcology

Abstract

fetched live from OpenAlex

In this study, degradation of polluted soil (with 1, 4 and 9% petroleum contamination) was performed using different microbial combinations and nutrients. Biosurfactant production and lipase production of the isolated microbes were also tested. A5 and A14 (isolated from petroleum-contaminated soil (PCS)) and G1 and G6 (isolated from garden soil) showed a positive result in the biosurfactant production test. A1, A5, A6, A13, A14, A15, A16, G1 and G5 showed zones around agar wells; these bacterial strains produced lipase. Combinations of microbes were applied in treatment of 1% PCS; the combination of only bacteria and the combination of bacteria and fungi showed petroleum degradation of 71 and 73·17%, respectively. In 4 and 9% PCS, the degradation activities of microbes decreased; the microbes needed a longer time to activate the large amount of petroleum. Furthermore, tests with and without nutrients were conducted to determine the effect of using nutrients in bioremediation of PCS. The results showed that petroleum degradation percentages from the treatment using nutrients were about 5% higher than the degradation percentages from treatments without nutrients.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.227

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 head0.003
GPT teacher head0.172
Teacher spread0.169 · 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 designObservational
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

Citations3
Published2019
Admission routes1
Has abstractyes

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