An Investigation of the Ability of a<sup>14</sup>C-Labeled Hydrocarbon Mineralization Test to Predict Bioremediation of Soils Contaminated with Petroleum Hydrocarbons
Bibliographic record
Abstract
ABSTRACT Bioremediation is a widely accepted technology for the remediation of hydrocarbon-contaminated soil. Treatability studies are usually carried out to assess the biodegradation potential of the contaminants and to design optimal treatments. Laboratory studies measuring soil respiration are often used. One method consists of monitoring the mineralization of a 14C-labeled hydrocarbon surrogate added to the contaminated soil. This study investigates the ability of this method to properly predict the removal of the hydrocarbon contaminants initially found in soils. Mineralization of 14C-labeled hexadecane was monitored in seven soils contaminated with various hydrocarbon mixtures, both fresh and weathered, in microcosm experiments. Reduction of total petroleum hydrocarbon (TPH) concentrations was measured simultaneously in separate microcosms. Both types of microcosms were subjected to the same amendment regimes. For all soils, poor correlation was observed between the mineralization and TPH reduction data sets. Mineralization data supported contaminants removal data in only one soil. Findings indicate that the radioactive surrogate method does not reliably predict the extent of, and the effect of amendments on, the removal of the hydrocarbons initially present in soil, and may therefore predict suboptimal treatment regimes. Recommendations for soil treatability protocols are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".