Bacterial Production Stimulated Across the Zone of Influence of a Ground Water Circulation Well in a BTEX‐Contaminated Aquifer
Bibliographic record
Abstract
Abstract Benzene, toluene, ethylbenzene, and xylene (BTEX) hydrocarbons are typically the most abundant carbon source for bacteria in gasoline‐contaminated ground water. In situ bioremediation strategies often involve stimulating bacterial heterotrophic production in an attempt to increase carbon demand of the assemblage. This may, in turn, stimulate biodegradation of contaminant hydrocarbons. In this study, ground water circulation wells (GCWs) were used as an in situ treatment for a fuel‐contaminated aquifer to stimulate bacterial production, purportedly by increasing oxygen transfer to the subsurface, circulating limiting nutrients, enhancing bioavailability of hydrocarbons, or by removing metabolically inhibitory volatile organics. Bacterial production, as measured by rates of bacterial protein synthesis, was stimulated across the zone of influence (ZOI) of a series of GCWs. Productivity increased from ∼10 2 to >10 5 ng C/L hour across the ZOI, suggesting that treatment stimulated overall biodegradation of carbon sources present in the ground water. However, even if BTEX carbon met all bacterial carbon demand, biodegradation would account for <4.3% of the total estimated BTEX removed from the ground water. Although bacterial productivity measurements alone cannot prove the effectiveness of in situ bioremediation, they can estimate the maximum amount of contaminant that may be biodegraded by a treatment system.
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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 | 0.000 | 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".