Intrinsic bioremediation of diesel-contaminated cold groundwater in bedrock
Bibliographic record
Abstract
In 1982 diesel fuel migrated from a well lease site into underlying fractured bedrock contaminating the groundwater-bearing zone approximately 30 m below ground surface. Historical contaminant concentrations, geochemical indicators, and microbiological data were analysed to evaluate natural attenuation, specifically intrinsic bioremediation. Evidence of microbial activity was provided by most probable number (MPN) and commercial biological activity reaction tests (BART). Laboratory microcosms using groundwater from the site were incubated under aerobic and anaerobic conditions with electron acceptor and nutrient amendment to assess microbial activity. Aerobic biodegradation rates were determined by measuring mineralization of 14C-dodecane. Nutrient amendment combined with a higher temperature (28 °C) increased the first-order aerobic biodegradation rate to 0.0066 d–1 from 0.0002 d–1 at 10 °C. Anaerobic biodegradation rates were calculated from depletion of total extractable hydrocarbons (TEH) over 717 d at 10 °C. Nutrient addition increased the anaerobic first-order biodegradation rate to 0.0016 from 0.0005 d–1. Data from these laboratory microcosms indicate that the current slow rates of intrinsic bioremediation may be enhanced with nutrient addition. Key words: intrinsic bioremediation, natural attenuation, groundwater, diesel fuel, hydrocarbon biodegradation, anaerobic hydrocarbon biodegradation, biological activity reaction test (BART).
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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".