Laboratory testing and microanalysis of colloidal mobilization from a glacial till
Bibliographic record
Abstract
The effect of the dispersion and piping of clay fines — colloid mobilization — as a means of remediating subsurface contamination was investigated. Prior colloid mobility research has focused on mitigating colloidal movement to reduce contaminant transport or monitoring colloids to determine the extent of mobility and contamination transport. To date, limited work has been performed to evaluate remediation efficiency associated with the removal of clay-sized (colloidal) particles and related sorbed contamination. Laboratory flow-through tests were performed on a glacial till from a radioactive cesium contaminated site. Post-testing analysis comprised mass balance checks, grain-size distribution testing, and microscopic analysis of removed particles. Results showed the potential of initiating and maintaining clay dispersion and piping, thus establishing a means to mobilize colloids and subsequently remove them from the subsurface environment. Beneficial effects of hydraulic gradient and ionic strength manipulations were influenced by soil properties, such as cation exchange capacity, clay content, and average pore diameter. In general, colloid mobilization testing results found that extreme levels of mobilization typically resulted in particle trapping and an overall reduction in colloidal removal, while the effect of more moderate levels was mobilization with increased rates of removal.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".