A CASE STUDY OF DNAPL REMEDIATION IN NORTHEASTERN BRAZIL
Bibliographic record
Abstract
Aquifer restoration in the United States is recognized as a technically challenging objective when dense non-aqueous phase liquids (DNAPLs) are present (1). In fact, only a few aquifers impacted by DNAPLs have been restored. Factors that have typically contributed to the lack of successful aquifer restoration include the chemical properties of the DNAPL, the physical properties of the aquifer, the absence of cost-effective technologies, and an incomplete or inaccurate development of a conceptual hydrogeological model for the site. In Latin America, environmental studies historically have been related to biological quality of surface water and groundwater. Recently, the U.S. and Canada have experienced an increased influx of foreign students and professionals interested in studying specialized courses in environmental engineering, or participating in conferences. This exposure to current topics has strengthened the awareness of these professionals regarding groundwater contamination from gasoline-derived compounds and chlorinated solvents. As a result of this increased awareness, Latin American hydrogeologists and environmental regulators have been able to recognize the potential problems that could result from DNAPL spills that may impact groundwater and have learned to approach them using locally available technology and resources. A case study of such an example is presented below.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".