Assessing <i>in situ</i> rates of anaerobic hydrocarbon bioremediation
Bibliographic record
Abstract
Identifying metabolites associated with anaerobic hydrocarbon biodegradation is a reliable way to garner evidence for the intrinsic bioremediation of problem contaminants. While such metabolites have been detected at numerous sites, the in situ rates of anaerobic hydrocarbon decay remain largely unknown. Yet, realistic rate information is critical for predicting how long individual contaminants will persist and remain environmental threats. Here, single-well push-pull tests were conducted at two fuel-contaminated aquifers to determine the in situ biotransformation rates of a suite of hydrocarbons added as deuterated surrogates, including toluene-d(8), o-xylene-d(10), m-xylene-d(10), ethylbenzene-d(5) (or -d(10)), 1, 2, 4-trimethylbenzene-d(12), 1, 3, 5-trimethylbenzene-d(12), methylcyclohexane-d(14) and n-hexane-d(14). The formation of deuterated fumarate addition and downstream metabolites was quantified and found to be somewhat variable among wells in each aquifer, but generally within an order of magnitude. Deuterated metabolites formed in one aquifer at rates that ranged from 3 to 50 µg l(-1) day(-1), while the comparable rates at another aquifer were slower and ranged from 0.03 to 15 µg l(-1) day(-1). An important observation was that the deuterated hydrocarbon surrogates were metabolized in situ within hours or days at both sites, in contrast to many laboratory findings suggesting that long lag periods of weeks to months before the onset of anaerobic biodegradation are typical. It seems clear that highly reduced conditions are not detrimental to the intrinsic bioremediation of fuel-contaminated aquifers.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".