Choice of Remediation Technology for a Contaminated Soil by 1,2-Dichloroethane (DCA)
Bibliographic record
Abstract
In Mexico, there are 635 sites contaminated by hazardous waste, due to the fact that a few years ago there was no legislation to support and guarantee environmental protection. This led to decades of contamination of soils and bodies of water. In the following case study, landfills were identified where 1,2-dichloroethane was stored, generating contaminated soil in this location, even affecting subway water bodies. The aim of this work is to identify the technologies for the remediation of contaminated soils, taking into account the affected site, the characteristics of the residue, costs and time, in order to determine the most effective and ideal technology. The Federal Remediation Technologies Roundtable (FRTR) will be considered as the counterpart for the selection of treatment technologies, published by the Remediation Bureau. The best technology for site remediation is "Soil Vapor Extraction", being the most ideal and efficient in terms of time and cost, and generating a high impact remediation outcome. The counterpart (FRTR) is considered to be a support tool that provides the most appropriate technologies for the remediation of a contaminated site.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".