The Effect of an Adsorbent Matrix on Recovery of Microorganisms from Hydrocarbon-Contaminated Groundwater
Bibliographic record
Abstract
The microbial degradation of recalcitrant hydrocarbons is an important process that can contribute to the remediation of oil and gas-contaminated environments. Due to the complex structure of subsurface terrestrial environments, it is important to identify the microbial communities that may be contributing to biodegradation processes, along with their abilities to metabolize different hydrocarbons in situ. In this study, a variety of adsorbent materials were assessed for their ability to trap both hydrocarbons and microorganisms in contaminated groundwater. Of the materials tested, a porous polymer resin (Tenax-TA) recovered the highest diversity of microbial taxa in preliminary experiments and was selected for additional (microcosm-based) testing. Oxic and anoxic experiments were prepared with groundwater collected from a contaminated aquifer to assess the ability of Tenax-TA to adsorb two environmental hydrocarbon contaminants of interest (toluene and benzene) while simultaneously providing a surface for microbial growth and hydrocarbon biodegradation. Microorganisms in oxic microcosms completely degraded both targets within 14 days of incubation, while anoxically-incubated microorganisms metabolized toluene but not benzene in less than 80 days. Community analysis of Tenax-TA-associated microorganisms revealed taxa highly enriched in sessile hydrocarbon-degrading treatments, including Saprospiraceae, Azoarcus, and Desulfoprunum, which may facilitate hydrocarbon degradation. This study showed that Tenax-TA can be used as a matrix to effectively trap both microorganisms and hydrocarbons in contaminated environmental systems for assessing and studying hydrocarbon-degrading microorganisms of interest.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".