ENVIRONMENTAL MANAGEMENT SCIENCE PROGRAM PROJECT NUMBER 87016 CO-PRECIPITATION OF TRACE METALS IN GROUNDWATER AND VADOSE ZONE CALCITE: IN SITU CONTAINMENT AND STABILIZATION OF STRONTIUM-90 AND OTHER DIVALENT METALS AND RADIONUCLIDES AT ARID WESTERN DOE SITES
Bibliographic record
Abstract
Radionuclide and metal contaminants are present in the vadose zone and groundwater throughout the U.S. Department of Energy (DOE) weapons complex. In situ containment and stabilization of these contaminants in vadose zones or groundwater is a cost-effective treatment strategy. Our facilitated approach relies upon the hydrolysis of introduced urea to cause the acceleration of calcium carbonate precipitation (and trace metal coprecipitation) by increasing groundwater pH and alkalinity (Fujita et al., 2000; Warren et al., 2001). Subsurface urea hydrolysis is catalyzed by the urease enzyme, which may be either introduced with the urea or produced in situ by ubiquitous subsurface urea hydrolyzing microorganisms. Because the precipitation processes are irreversible and many western aquifers are saturated with respect to calcite, the co-precipitated metals and radionuclides will be effectively removed from groundwater. The rate at which trace metals are incorporated into calcite is a function of calcite precipitation kinetics, adsorption interactions between the calcite surface and the trace metal in solution (Zachara et al., 1991), solid solution properties of the trace metal in calcite (Tesoriero and Pankow, 1996), and also the surfaces upon which the calcite is precipitating. A fundamental understanding of the coupling of calcite precipitation and trace metal partitioning, and how this occurs in aquifers and vadose environments is lacking. This report summarizes work undertaken during the second year of this project.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.279 | 0.104 |
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".