Implementation of solidification/stabilization process to reduce hazardous impurities and stabilize soil matrices
Bibliographic record
Abstract
A wide variety of technologies is available for the treatment of contaminated soil in both the vadose zone (originating above the water table) and saturated zone (originating below the water table). Several processes involve immobilizing soil contaminants by physically, chemically or biologically. Among them, a wide range of wastes, both solids and liquids, are being treated by "solidification/stabilization" (S/S). In solidification, by adding binding reagents, physical state of the waste being changed by encapsulating a waste to form a solid material from liquid as well as to restrict contaminant migration to leaching by decreasing the exposed surface area. Whereas stabilization through chemical reactions immobilizes the hazardous materials by reducing them to less soluble or toxic form. Characteristics of different types of reagents/additives of S/S technology both from inorganic and organic origin are presented in this paper. In-situ and ex-situ application of S/S technology and their advantages-disadvantages are discussed with basic approaches. Finally, introducing with internal and external factors influencing the long-term durability of S/S treated materials as well as monitoring & treatment management of it after processing are briefly presented
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".