Polar organic chemical integrative sampler (POCIS) allows compound specific isotope analysis of substituted chlorobenzenes at trace levels
Bibliographic record
Abstract
Compound specific isotope analysis (CSIA) is an established tool to demonstrate in situ degradation of traditional groundwater contaminants at heavily contaminated sites, usually at mg/L range aqueous concentrations. Currently, an efficient preconcentration method is lacking to expand CSIA to low aqueous concentration environmental samples. This work demonstrated the compatibility of polar organic chemical integrative sampler (POCIS) with CSIA of C, H, and N isotopes for four NH2- and NO¬2-substituted chlorobenzenes at low μg/L. Diffusion and sorption showed insignificant carbon isotope fractionation (<0.7‰) in laboratory experiment, except for a reproducible shift of 1.6‰ for 3,4-dichloronitrobenzene. A similar constant reproducible shift of 0.8-2‰ was evident for N-CSIA. Whereas, the compatibility of POCIS for H-CSIA seems to be analyte specific possibly reflecting the adsorption mechanism to POCIS by H-bonding. Performance of the POCIS-CSIA method was evaluated in a pilot constructed wetland where comparable C- and N-CSIA results were obtained from grab sampling and POCIS. This work opens the potential of CSIA application to the low concentration polar emerging contaminants in the environment, such as pesticides, pharmaceuticals, and flame-retardants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".