Combining Remediation Techniques Increases Kinetics for Removal of Persistent Organic Contaminants from Soil
Bibliographic record
Abstract
A number of techniques, both mechanical and biological, have been investigated for the remediation of persistent organic contaminants from soils. However, most of these techniques have been applied independently. As a consequence of using only one process, remediation usually is slow for persistent organic contaminants. To improve remediation, multiple techniques that complement different aspects of contaminant removal have been applied to soils in combination. This resulted in an enhanced multi-process phytoremediation system that improved and accelerated the overall remediation resulting in removal of 95% of total PAHs. The remediation system includes physical (volatilization), photochemical (photooxidation), microbial degradation and plant growth (phytoremediation) processes. The techniques applied to realize these processes are land fanning (aeration and light exposure), microbial remediation (introduction of contaminant degrading bacteria) and phytoremediation (plant growth with plant growth promoting rhizobacteria). This system was very effective at removal of persistent, strongly bound contaminants from soil. It appears that the combination of these components may be a viable solution for remediating persistent organic contaminants from soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".