Determination of distribution coefficient of geomaterials and immobilizing agents
Bibliographic record
Abstract
The fate and transport of reactive contaminant(s) in geomaterials (i.e., soils and rocks) and efficacy of the immobilization–remediation methodology of the contaminated geomaterials is governed mainly by the sorption and desorption characteristics of these geomaterials and immobilizing agents. As such, establishment of these characteristics becomes mandatory. To achieve this, batch tests are usually conducted and a distribution coefficient is obtained from Freundlich, linear, and Langmuir isotherms. However, the relative efficiency of these isotherms needs to be ascertained for selection of the appropriate distribution coefficient for a contaminant geomaterial – immobilizing agent system. With this in mind, conventional batch tests were conducted on various geomaterials and immobilizing agents, to establish their sorption and desorption characteristics. However, it should be noted that batch tests do not represent the real-life contaminant geomaterial – immobilizing agent interaction, and are quite cumbersome and time-consuming. Under these circumstances, correlating sorption and desorption characteristics of these materials with the electrical conductivity of their solutions seems to be a potential alternative. Utility of this concept for modeling real-life contaminant geomaterial – immobilizing agent interaction has also been demonstrated very well in this study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".