Contaminant Sorbent Aggregation Index based on Cadmium Sorption Capacity
Bibliographic record
Abstract
Mixture rules that enable the estimation of the characteristics of composite mixtures using data on the characteristics of their components are useful in reducing the frequency and cost of material testing for construction quality assurance prediction of barriers in waste containment systems. Different components of a mixture have characteristics that may not always be represented in direct proportion to their contents in the composite mixture. In this paper, the interaction effects of such mixtures are scaled in terms of an aggregation index. This index is formulated on the basis of metal sorption capacity measurements and used to investigate four mix designs, covering different weight proportions of four materials (Ottawa sand, fly ash, diatomaceous earth and Ca-montmorillonite). Computations using test data obtained through cadmium sorption tests indicate an increase in aggregation by up to 50% for mixtures with high clay content (10%). Also, values of aggregation index were found to be less than 1 for mixtures with low clay content, indicating an increase in cadmium sorption beyond theoretical levels that are based on mix component proportions and their sorption capacities. Presumably, textural changes after material mixing increased the measured specific surface of the composite materials relative to the theoretically computed value. This result is attributed to disruption of cohesion in the clay fraction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".