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Evidence of Substantial Carbon Isotope Fractionation among Substrate, Inorganic Carbon, and Biomass during Aerobic Mineralization of 1,2-Dichloroethane by <i>Xanthobacter autotrophicus</i>

2000· article· en· W2163756337 on OpenAlexaff
Daniel Hunkeler, Ramón Aravena

Bibliographic record

VenueApplied and Environmental Microbiology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversity of Waterloo
FundersU.S. Environmental Protection Agency
KeywordsFractionationChemistryIsotopes of carbonIsotope fractionationMineralization (soil science)Carbon fibersIsotopeEnvironmental chemistryChromatographyTotal organic carbonOrganic chemistryNitrogenMaterials science

Abstract

fetched live from OpenAlex

Carbon isotope fractionation during aerobic mineralization of 1, 2-dichloroethane (1,2-DCA) by Xanthobacter autotrophicus GJ10 was investigated. A strong enrichment of (13)C in residual 1,2-DCA was observed, with a mean fractionation factor alpha +/- standard deviation of 0.968 +/- 0.0013 to 0.973 +/- 0.0015. In addition, a large carbon isotope fractionation between biomass and inorganic carbon occurred. A mechanistic model that links the fractionation factor alpha to the rate constants of the first catabolic enzyme was developed. Based on the model, it was concluded that the strong enrichment of (13)C in 1,2-DCA arises because the first irreversible step of the initial enzymatic transformation of 1,2-DCA consists of an S(N)2 nucleophilic substitution. S(N)2 reactions are accompanied by a large kinetic isotope effect. The substantial carbon isotope fractionation between biomass and inorganic carbon could be explained by the kinetic isotope effect associated with the initial 1,2-DCA transformation and by the metabolic pathway of 1,2-DCA degradation. Carbon isotope fractionation during 1,2-DCA mineralization leads to 1,2-DCA, inorganic carbon, and biomass with characteristic carbon isotope compositions, which may be used to trace the process in contaminated environments.

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 categoriesInsufficient payload (model declined to judge)
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 score0.280
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.175
Teacher spread0.170 · 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.

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

Citations69
Published2000
Admission routes1
Has abstractyes

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