Framework for field-scale application of molecular biological tools to support natural and enhanced bioremediation
Bibliographic record
Abstract
Microorganisms naturally present at environmental contaminated sites are capable of biodegrading, biotransforming, or removing contaminants in soil and groundwater through bioremediation processes. Cleanup strategies and goals for site remediation can be effectively achieved by bioremediation leveraging the capabilities of microorganisms to biotransform contaminants into lesser or non-toxic end products; however, reproducible success can be limited by inadequate design or performance monitoring. A group of biological analyses collectively termed molecular biological tools (MBTs) can be used to assess the contaminant-degrading capabilities and activities of microorganisms present in the environment and appropriately implement bioremediation approaches. While successful bioremediation has been demonstrated through previously described lab-scale studies and field-scale implementation for a variety of environmental contaminants, design and performance monitoring of bioremediation has often been limited to inferring biodegradation potential, occurrence, and pathways based on site geochemistry or lab-scale studies. Potential field-scale application of MBTs presents the opportunity to more precisely design and monitor site-specific bioremediation approaches. To promote standardization and successful implementation of bioremediation, a framework for field-scale application of MBTs within a multiple lines of evidence (MLOE) approach is presented. The framework consists of three stages: (i) "Assessment" to evaluate naturally occurring biogeochemical conditions and screen for potential applicability of bioremediation, (ii) "Design" to define a site-specific bioremediation approach and inform amendment selection, and (iii) "Performance Monitoring" to generate data to measure or infer bioremediation progress following implementation. This framework is introduced to synthesize the complexities of environmental microbiology and guide field-scale application of MBTs to assess bioremediation potential and inform site decision-making.
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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 | 0.021 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".