Using cell yields and qPCR to estimate biotic contribution to 1,1,1-trichloroethane dechlorination at a field site treated with granular zero valent iron and guar gum
Bibliographic record
Abstract
Abstract Chlorinated ethanes are environmental pollutants found frequently at many contaminated industrial sites. 1,1,1-Trichloroethane (1,1,1-TCA) can be dechlorinated and detoxified via abiotic transformation or biologically by the action of dechlorinating microorganisms such as Dehalobacter ( Dhb ). At a field site, it is challenging to distinguish abiotic vs biotic mechanisms as both processes share common transformation products. In this study, we evaluated using the Dhb 16S rRNA gene and specific reductive dehalogenase genes as biomarkers for 1,1,1-TCA and 1,1-dichloroethane (1,1-DCA) dechlorination. We analyzed samples from laboratory groundwater microcosms and from an industrial site where a mixture of granular zero valent iron (ZVI) and guar gum was injected for 1,1,1-TCA remediation. Abiotic and biotic transformation products were monitored and the changes in dechlorinating organisms were tracked using quantitative PCR (qPCR) with primers targeting the Dhb 16S rRNA gene and two functional genes cfrA and dcrA encoding enzymes that dechlorinate 1,1,1-TCA to 1,1-DCA and 1,1-DCA to chloroethane (CA), respectively. The abundance of the cfrA - and dcrA -like genes confirmed that the two dechlorination steps were carried out by two distinct Dhb populations at the site. Using cell yields established in laboratory microcosms along with measured abundances of the Dhb 16S rRNA gene in site samples, biotic and abiotic transformation of 1,1,1-TCA at the site was estimated. The biomarkers used in this study proved useful for tracking biodechlorination of 1,1,1-TCA and 1,1-DCA where both abiotic (e.g. with ZVI) and biotic processes co-occur.
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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 | 0.000 | 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".