Multi-element compound specific isotope analysis reveals aerobic biodegradation of 2,3-dichloroaniline at a complex site
Bibliographic record
Abstract
Compound specific isotope analysis (CSIA) is an established tool for evaluating in situ transformation of organic contaminants. To date, CSIA has never been applied to understand the in situ fate of 2,3-dichloroaniline (2,3-DCA). Although persistent in the environment, several microorganisms were identified as able to degrade 2,3-DCA, thus making this contaminant a potential candidate for bioremediation. Using a controlled-laboratory experiment, we determined, for the first time, negligible carbon and hydrogen isotope fractionation, and a significant inverse nitrogen isotope effect during aerobic 2,3-DCA biodegradation via dioxygenation using a mixed enrichment culture. The corresponding AKIEN values ranged from 0.9938±0.0003 to 0.9922±0.0004. The ε_(N,bulk) values, ranging from +6.2±0.3 to +7.9±0.4‰ was applied to investigate the potential in situ 2,3-DCA biotransformation at a contaminated site, where the field-obtained carbon and nitrogen isotope signatures suggested aerobic biotransformation by native microorganisms. Under the assumption of the applicability of the Rayleigh model at the field site, the extent of 2,3-DCA transformation was estimated at up to 80 to 90%. This study proposes multi-element CSIA of 2,3-DCA as a novel application to study 2,3-DCA fate in groundwater and surface water.
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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 | 0.001 | 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) | 0.001 | 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".