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Record W2013331223 · doi:10.1021/es990797y

Carbon Isotope Fractionation during Anaerobic Biodegradation of Toluene:  Implications for Intrinsic Bioremediation

2000· article· en· W2013331223 on OpenAlexafffund
Jason M. E. Ahad, Barbara Sherwood Lollar, Elizabeth A. Edwards, G. F. Slater, Brent E. Sleep

Bibliographic record

VenueEnvironmental Science & Technology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiodegradationTolueneChemistryIsotope fractionationIsotope analysisFractionationEnvironmental chemistryBioremediationIsotopes of carbonSulfateCarbon fibersChromatographyTotal organic carbonOrganic chemistryContaminationGeologyMaterials scienceEcology

Abstract

fetched live from OpenAlex

Carbon isotope fractionation produced by anaerobic biodegradation of toluene was evaluated in laboratory experiments under both methanogenic and sulfate-reducing conditions. A small (∼2‰) but highly reproducible 13 C-enrichment in the residual toluene at advanced stages of microbial transformation was observed in both cultures. The maximum isotopic enrichment observed in the residual toluene was +2.0‰ and +2.4‰ for the methanogenic and sulfate-reducing cultures, respectively, corresponding to isotopic enrichment factors (ε) of −0.5 and −0.8. Because the accuracy and reproducibility associated with gas chromatograph−combustion−isotope ratio mass spectrometry (GC/C/IRMS) is ±0.5‰, delineating which of these two terminal electron-accepting processes (TEAP) is responsible for the biodegradation of toluene at field sites will not be possible. However, the potential does exist to use compound-specific isotope analysis (CSIA), in conjunction with other methodologies, as a means of validating advanced stages of intrinsic bioremediation in anaerobic systems. Caution is urged that relating this small (∼2‰) fractionation to biodegradation at complex field sites will prove a challenge.

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 score0.679
Threshold uncertainty score0.790

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 head0.203
Teacher spread0.199 · 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

Citations114
Published2000
Admission routes2
Has abstractyes

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