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Record W2088450512 · doi:10.1139/w07-052

Microbial surfactant activities from a petrochemical landfarm in a humid tropical region of Brazil

2007· article· en· W2088450512 on OpenAlexvenueno aff
Bianca Mendes Maciel, João Carlos Teixeira Dias, Ana Cácia Freire dos Santos, Ronaldo Costa Argôlo Filho, Renato Fontana, Leandro Lopes Loguercio, Rachel Passos Rezende

Bibliographic record

VenueCanadian Journal of Microbiology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado da BahiaUniversidade Estadual de Santa CruzConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsPulmonary surfactantMicroorganismBioremediationPetrochemicalPetroleumContaminationSoil contaminationTropicsEnvironmental scienceEnvironmental chemistryChemistryBiologyBacteriaEcologyEnvironmental engineeringBiochemistry

Abstract

fetched live from OpenAlex

The goal of this study was to assess the presence and surfactant potential of naturally occurring microbes from a tropical soil with petrochemical contamination. Microorganisms in a soil sample from a Brazilian landfarm were isolated and grown on petroleum as the sole carbon source. Of 60 isolates screened for petroleum-based growth, 7 demonstrated surfactant activities by the drop-collapse methodology over various types of oils. From their growth profiles in liquid culture during 132 h, all had their first detection of surfactant activity after 96 h. Little is currently known about biosurfactant-producing microorganisms in tropical environments contaminated by hydrophobic compounds, and the search for them is essential for bioremediation and for oil recovery enhanced by microbes. Our results indicate that different petroleum-grown microorganisms showing surfactant activity can be recovered from landfarm soil in a tropical environment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2007
Admission routes1
Has abstractyes

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