Effects of Gas Generation and Precipitates on Performance of Fe° PRBs
Bibliographic record
Abstract
Long-term reactivity and permeability are critical factors in the performance of granular iron permeable reactive barriers (PRBs). Thus it is a topic of great practical importance, as well as scientific interest. In this study, four types of source solutions (distilled H2O, 10 mg/L TCE, 300 mg/L CaCO3, and 10 mg/L TCE + 300 mg/L CaCO3) were supplied to four columns containing a commercial granular iron material. In all four columns, gases accumulated to approximately 10% of the initial porosity and resulted in declines in permeability of approximately 50% to 80%. In the columns receiving CaCO3, carbonate precipitates accumulated to approximately 7% of the initial porosity, with no apparent decline in permeability. The data indicate that precipitates formed initially at the influent ends of the columns, reducing the reactivity of the iron in this region. As a consequence of the reduced reactivity, calcium and bicarbonate migrated further into the column, to precipitate in a region where the reactivity remained high. Thus precipitation occurred as a moving front through the columns. The results suggest improved methods for PRB design and rehabilitation, and also suggest improvements that are needed in the mathematical models developed for predicting long-term performance.
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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".