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Record W2064315191 · doi:10.1021/es0105111

Hydrogen and Carbon Isotope Fractionation during Aerobic Biodegradation of Benzene

2001· article· en· W2064315191 on OpenAlexafffund
Daniel Hunkeler, Nils Andersen, Ramón Aravena, Stefano M. Bernasconi, B. J. Butler

Bibliographic record

VenueEnvironmental Science & Technology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBenzeneBiodegradationEnvironmental chemistryIsotopes of carbonIsotope fractionationCarbon fibersChemistryIsotopeFractionationHydrogenTotal organic carbonOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

The main aim of the study was to evaluate hydrogen and carbon isotope fractionation during biodegradation of benzene as a possible tool to trace the process in contaminated environments. Aerobic biodegradation of benzene by two bacterial isolates, Acinetobacter sp. and Burkholderia sp., was accompanied by significant hydrogen and carbon isotope fractionation with hydrogen isotope enrichment factors of -12.8 +/- 0.7 per thousand and -11.2 +/- 1.8 per thousand, respectively, and average carbon isotope enrichment factors of -1.46 +/- 0.06 per thousand and -3.53 +/- 0.26 per thousand, respectively. Inorganic carbon produced by Acinetobacter sp. was depleted in 13C by 3.6-6.2 per thousand as compared to the initial delta13C of benzene, while the produced biomass was enriched in 13C by 3.8 per thousand. The secondary aim was to determine isotope ratios of benzenes from different manufacturers with regard to the use of isotopes for source differentiation. While two of the four analyzed benzenes had similar delta13C values, each of them had a distinct delta2H-delta13C pair and delta2H values spread over a range of 66.5 per thousand. Thus, combined analyses of hydrogen and carbon isotopes may be a more promising approach to trace sources and/or biodegradation of benzene than measuring carbon isotopes only.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
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 head0.004
GPT teacher head0.195
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

Citations164
Published2001
Admission routes2
Has abstractyes

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