Latest Findings in In-situ Remediation of Hydrocarbon Impacted Soils using Hydrogen Peroxide
Bibliographic record
Abstract
There has been a concerted effort over the last few years in Alberta to increase our level of understanding of hydrogen peroxide (H2O2) technology as an oxidation remediation technique for hydrocarbon-contaminated sites. H2O2 (and Fenton’s reagent- a mixture of H2O2 and iron salts) oxidizes contaminant hydrocarbons, and it degrades and releases oxygen and bio-stimulates the subsoils leading to a faster aerobic degradation. This paper gives an overview of the H2O2 technology and describes a successful field application that has subsequently led to a number of research projects at the University of Calgary. The field application proved economical and was successful in a restricted access situation (Mahmoud et al, 2000, 2003). Two research projects have been conducted since the completion of the field application. The first focused on examining the potential of H2O2 in causing volume change in hydrocarbon-contaminated silty sands (Mahmoud et al, 2003; Mohamed at al, 2002). The second has concentrated on examining the generation of gases leading to soil volume change, as well as determining the optimal volume of Fenton’s reagent required for remediating diesel-impacted soil. This latest study has also examined the zone of influence
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".