Bibliographic record
Abstract
Abstract In situ chemical oxidation (ISCO) is a mature treatment technology that involves the delivery of a chemical oxidant into a target treatment zone (TTZ) to destroy petroleum hydrocarbon (PHC) compounds, and thereby reduce risk to human health and the environment. Commonly used chemical oxidants include hydrogen peroxide, sodium percarbonate, ozone, sodium or potassium permanganate, and sodium or potassium persulfate. All these oxidants can degrade environmentally relevant PHCs except that permanganate is non-reactive toward benzene. Ozone is delivered into the TTZ as a gas while the other oxidants are typically delivered as a concentrated liquid. ISCO should be considered part of integrated remediation strategy and not used in isolation. This chapter provides a reader with an introduction to key aspects of ISCO that are relevant to applications at sites contaminated with PHCs. Following a discussion of the TTZ, it then examines the fundamentals of the common oxidants used. Next the interaction of chemical oxidants with aquifer materials is explored, and a description of relevant transport considerations is provided. This is followed with an overview of methods that can be used to deliver an oxidant to treat PHCs in a TTZ. This chapter closes with a summary of the important takeaway messages.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".