Field‐Scale <scp>qPCR</scp> Data to Estimate Rate Constants for Toluene Biodegradation in Groundwater
Bibliographic record
Abstract
Abstract Biodegradation of petroleum hydrocarbons in groundwater occurs naturally and can be enhanced to support contaminated site management and remediation. Molecular biological tools can be used to assess the occurrence of biodegradation and monitor bioremediation efforts; however, the use of specific genes (i.e., biomarkers) to identify the rate constants of contaminant removal has not been explored for monoaromatic hydrocarbons. In this study, an approach was developed to estimate an apparent first‐order rate constant for anaerobic biodegradation of toluene based on toluene concentration and the abundance of the alpha subunit of the benzylsuccinate synthase gene (i.e., bssA ). The utility of the approach was evaluated by comparing the distribution of estimated rate constants to those from a published compilation at benchmark sites. There was good agreement between the distribution of rate constants calculated from the abundance of gene copies from soil cores and the distribution of rate constants at benchmark sites, while rate constants calculated from the abundance of gene copies in groundwater were lower than those from the benchmark sites. Given that groundwater samples are more common and convenient to obtain, it is proposed to use the rate constants estimated from groundwater samples to document whether the microbial community has acclimated for biodegradation of toluene. When practitioners use models to evaluate risk at petroleum release sites, they often assume a “typical” rate constant for biodegradation. If the abundance of the bssA biomarker demonstrates that the microbial community has acclimated, then it is appropriate to select and use a rate constant from benchmark sites to forecast biodegradation and attenuation of toluene at a site being evaluated.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".