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Record W1728426699 · doi:10.1029/2006wr005264

Isotopic fractionation by diffusion in groundwater

2008· article· en· W1728426699 on OpenAlexaff
Eric M. LaBolle, Graham E. Fogg, Juana B. Eweis, Janko Gravner, Derek G. Leaist

Bibliographic record

VenueWater Resources Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsWestern University
Fundersnot available
KeywordsFractionationIsotope fractionationGroundwaterEquilibrium fractionationChemistryAqueous solutionDiffusionEnvironmental chemistryIsotope analysisGeologyChromatographyThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

During the last decade, isotopic fractionation has gained acceptance as an indicator of microbiological and chemical transformations of contaminants in groundwater. These transformation processes typically favor isotopically light, compared to isotopically heavy, contaminants, resulting in enrichment of the latter in the residual aqueous phase. In these isotope applications, it has been generally presumed that physical transport processes in groundwater have a negligible effect on isotopic enrichment. It is well known, however, that aqueous phase diffusion generally proceeds faster for isotopically light, compared to isotopically heavy, solute molecules, often resulting in isotopic fractionation in groundwater. This paper considers the potential for isotopic fractionation during transport in groundwater resulting from minute isotopic effects on aqueous diffusion coefficients. Analyses of transport in heterogeneous systems delimit the viable range of isotopic fractionation by diffusion in groundwater. Results show that diffusion can result in similar degrees of depletion and enrichment of isotopically heavy solutes during transport in heterogeneous systems with significant diffusion rate–limited mass transfer between fast‐ and slow‐flow zones. Additional analyses and examples explore conditions that attenuate the development of significant fractionation. Examples are presented for 13 C methyl tertiary butyl ether and deuterated and nondeuterated isopropanol and tertiary butyl alcohol using aqueous diffusion coefficients measured by the Taylor dispersion method with refractive index profiling as a part of this study. Examples elucidate the potential for diffusive fractionation as a confounder in isotope applications and emphasize the importance of hydrogeologic analysis for assessing the role of diffusive fractionation in isotope applications at contaminant field sites.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.002

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 head0.036
GPT teacher head0.286
Teacher spread0.250 · 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; both teacher heads agree on what is shown here.

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

Citations71
Published2008
Admission routes1
Has abstractyes

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