Bibliographic record
Abstract
To assess the long-term performance of soil-cement materials used in source-control remediation methods (i.e. cement-based solidification/stabilization), procedures to measure or estimate contaminant migration parameters are essential. Previous research indicates that diffusion may be an important mechanism in contaminant transport through soil-cement materials. However, there is a paucity of information regarding the diffusion of contaminants through these materials.\nThe development of a single-reservoir diffusion apparatus and methodology to assess the effective diffusion coefficient (De) and effective porosity (ne) of dissolved, conservative, inorganic chemicals for saturated, cured, monolithic soil-cement specimens is discussed. This is the only study known to investigate these parameters for these materials.\nThe results of tritiated water diffusion tests on 14 different soil-cement mixtures are presented and the influence of curing time and mixture properties such as water-to-cement ratio, cement content, and grain-size distribution are examined. Results suggest that, to determine reasonable assessments of the longer-term parameters, soil-cement samples should be cured for a minimum of 70 days before commencing diffusion testing. Values of ne (0.21 to 0.41) and De (2.50×10-10 m2/s to 7.0×10-10 m2/s) determined are similar to those previously determined for a number other low-hydraulic conductivity materials (i.e. saturated inactive clays).\nThe water content of the initial mixture is shown to have a substantial effect on the diffusive properties as the results indicate that both the total porosity (n) and the effective porosity, ne, generally increase with increasing initial water content. For the range of soils used in this investigation, grain-size distribution did not have a substantial effect on the values of ne or De determined from diffusion testing.\nThe adaptation of a double-reservoir diffusion testing apparatus and methodology to evaluate the distribution coefficient (Kd) and De of organic contaminants is also presented. This apparatus is used to evaluate Kd and De of benzene, ethylbenzene, naphthalene, and trichloroethylene for three soil-cement mixtures. Values of Kd (0 to 2.5 cm3/g depending on the compound and soil-cement mixture tested) determined from diffusion testing, batch testing, and theoretical estimates from the literature were in general agreement. Values of De for the organic compounds ranged from 1.50×10-10 to 3.0×10-10 m2/s.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".