Sorption- and diffusion-induced isotopic fractionation in chloroethenes
Bibliographic record
Abstract
Isotopic fractionation of groundwater contaminants can occur due to degradation, diffusion and sorption. Of these, only degradation has been extensively explored, yet diffusive isotopic fractionation (DIF) and sorptive isotopic fractionation (SIF) can have significant effects on the isotopic enrichment of groundwater contaminants. Understanding how to mathematically describe and model these processes is vital to the correct interpretation of compound-specific isotope analysis (CSIA) data in the field. Here, models for these physical fractionation processes are developed and described, including the definition of a sorption enrichment factor. These models are then implemented numerically using inverse and finite-element methods to investigate two scenarios, diffusion-sorption and diffusion-sorption-advection, that have been measured in the laboratory. Concentration, δ37Cl, and δ2H data from cis-dichloroethene (cDCE) and trichloroethene (TCE) are used as inputs to the models. Unknown transport parameters including diffusive fractionation exponents are determined from an inverse modelling approach. DIF is shown to have a stronger influence on chlorine isotopologues than on hydrogen isotopologues. For both cDCE and TCE, the sorption enrichment factor of chlorine is found to be negative while that of hydrogen is positive. The presented approach and results provide novel tools and insight into DIF and SIF and underline that these processes should be taken into account when using CSIA to assess contaminant fate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".