Quantification of Scale and Monolith Surface Exposure Effects on Contaminant Leaching from Flowable Fill
Bibliographic record
Abstract
The volumetric specific surface (S/V) of monoliths influences contaminant leachability. Small samples such as those used in laboratory-based leaching tests have higher S/V ratios than larger monoliths of controlled low-strength materials (CLSM) in utility and energy pipeline trenches. Quantitative relationships are herein derived for relating contaminant leaching rates from full-scale monolith-filled trenches in the field to laboratory leaching data. A field dimensional exposure modification factor, P, is derived with a magnitude range of 37.6 to 50.37 for trenches with length and cross-sectional area dimensional ranges of 10–15 m and 1–4 m, respectively. Then, P, which increases with CLSM sectional area, is applied to copper, arsenic, and selenium leaching data for CLSM comprising portland cement, aggregate, water, and fly ash, ranging in weight contents from 5 to 20%. The results of leaching with water and pH 5.5 leachant show that computed diffusion coefficients for the metals in the field, D ef values are higher than values obtained for small samples through leaching tests in the laboratory. Furthermore, D ef values are higher at low ash substitution levels than at higher levels. The highest D ef values, which are at 5% ash content, are 2.09 × 10−4 m2/s (deionized water), 7.29 × 10−11 m2/s (pH 5.5) and 2.09 × 10−10 m2/s (pH 5.5) for copper, arsenic, and selenium, respectively. Apparently, at higher ash contents, cementation effects decrease monolith porosity to produce lower values of diffusion coefficient. Computed estimates of cumulative leaching fractions at high saturation are low for the contaminants and are directly proportional to D ef values.
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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 | 0.000 | 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".