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Record W2287382567 · doi:10.1080/09593330.2015.1102333

Halotolerance and effect of salt on hydrophobicity in hydrocarbon-degrading bacteria

2015· article· en· W2287382567 on OpenAlexafffund
Adégilns Longang, Christopher B. Buck, Kathlyn M. Kirkwood

Bibliographic record

VenueEnvironmental Technology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHalotoleranceHydrocarbonBioremediationBiodegradationBacteriaEnvironmental chemistryMicroorganismChemistrySalt (chemistry)ContaminationSalinityFlocculationMicrobiologyBiologyEcologyOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrocarbon-contaminated environments often also experience co-contamination with elevated levels of salt. This paper investigates the occurrence of halotolerance among several hydrocarbon-degrading bacteria, as an initial assessment of the importance of salt contamination to bioremediation strategies. Halotolerance was common, but not ubiquitous, among the 12 hydrocarbon-degrading bacteria tested, with many strains growing at up to 75 or 100 g NaCl L(-1) in rich medium. Greater sensitivity to elevated salt concentrations was observed among aromatics degraders compared to saturates degraders, and in defined medium compared to rich medium. Observed effects of high salt concentrations included increased lag times and decreased maximum growth. Many strains exhibited flocculation at elevated salt concentrations, but this did not correlate to any patterns in cell surface hydrophobicity, measured using the Bacterial Adhesion to Hydrocarbon assay. The occurrence of halotolerance in hydrocarbon-degrading bacteria suggests the potential for native microorganisms to contribute to the bioremediation of oil and salt co-contaminated sites, and indicates the need for a better understanding of the relationship between halotolerance and hydrocarbon biodegradation capabilities.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.504

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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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

Citations5
Published2015
Admission routes2
Has abstractyes

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