<i>Dehalobacter</i> Dechlorinates Dichloroanilines and Contributes to the Natural Attenuation of Dichloronitrobenzenes at a Complex Industrial Site
Bibliographic record
Abstract
The potential for bioremediation of dichloroanilines (2,3- and 3,4-DCA) and dichloronitrobenzenes (3,4-, 2,5-, and 2,3-DCNB) was investigated using inocula from an industrial site in the northeast of Brazil. Anaerobic biotransformation of these chlorinated compounds was observed in microcosms simulating site conditions, particularly when an electron donor was added. To disentangle specific transformation reactions, subcultures from active anaerobic microcosms were enriched with individual DCA or DCNB isomers. In these single-compound enrichment cultures, DCNB isomers were reduced to the corresponding DCA. Each DCA isomer was stoichiometrically dechlorinated to the corresponding monochloroaniline (CA) isomers. Further dechlorination of CA to aniline was noted. The reduction of DCNB to DCA was sustained by known fermenters and possibly Desulfitobacterium, while Dehalobacter was clearly responsible for the dechlorination of DCAs. These findings helped attribute function to the microbial community data retrieved from field samples to improve the conceptual site model and enable the identification of bioactive zones and rate-limiting conditions at a complex site. More generally, these data broaden the understanding of the metabolic repertoire of Dehalobacter and provide a model for incorporating microbial community data into site remediation efforts.
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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 | 0.001 | 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".