Diffusion Sampler for Compound Specific Carbon Isotope Analysis of Dissolved Hydrocarbon Contaminants
Bibliographic record
Abstract
Compound Specific Isotope Analysis (CSIA) is widely utilized to study the fate of organic contaminants in groundwater. To date, however, no method is available to obtain CSIA samples at a fine (cm) spatial scale across the sediment-surface water interface (SWI), a key boundary for discharge of contaminated groundwater to surface water. Dissolved contaminants in such discharged zones undergo rapid temporal and spatial changes due to heterogeneity in redox conditions and microbial populations. The compatibility of a passive sediment pore water sampler ("peeper") to collect 40 mL samples for CSIA of benzene, toluene, monochlorobenzene, and 1,2-dichlorobenzene at field-relevant concentrations (0.1-5 mg L(-1)) was evaluated in laboratory experiments. Results demonstrate that physical diffusion across the polysulfone membrane does not alter the carbon isotope values (±0.5‰). Measured δ(13)C values also remain invariant despite significant adsorption of the compounds on the peeper material, an effect which increased with higher numbers of chlorine atoms and sorption coefficient (Koc) values. In addition, isotope equilibrium between the peeper chamber and the sediment pore water occurred in less than a day, indicating the peeper method can be used to provide samples for CSIA analysis at fine spatial and temporal sampling resolutions in contaminated sediments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".