Parameter Estimation of Aqueous Contaminant Transport and Storage in Heterogeneous, Alluvial Aquifers
Bibliographic record
Abstract
The process of back diffusion is emerging as a major factor constraining restoration of sites impacted by recalcitrant compounds. Back diffusion maintains plumes downgradient of sources of contaminants even after the source has been depleted. Ability to predict outcomes of site cleanup is important to managing the current legacy of anthropogenic releases. There are transport models available that include diffusion. These models could be used to make a priori predictions of benefits achieved by upgradient source depletion – if there were a way to obtain the input parameters needed. Single well injection-withdrawal (SWIW) tracer tests are a promising area of research that may provide the needed parameters to predict cleanup benefits a priori. Bench-scale research is underway at Colorado State University’s Center for Contaminant Hydrology to determine the necessary methodology of SWIW tests. Two short-duration, dual-tracer SWIW tests were done: one in homogeneous media obtained from the Borden aquifer in Canada and the other in a heterogeneous architecture with media obtained from F. E. Warren Air Force Base in Wyoming. Fluorescein and bromide were used as tracers. The tank tests were instructive in resolving better implementation techniques. Determination of good SWIW methodology will be a step toward the goal of reliable a priori predictions of benefits achieved by upgradient contaminant flux depletion. Thus, better tools will be available for decision-making and management of expectations related to site cleanup. 1 Graduate Research Assistant, Department of Civil Engineering, Colorado State University, Fort Collins, CO 80523-1320, laruther@engr.colostate.edu 2 Research Scientist, Department of Civil Engineering, Colorado State University, Fort Collins, CO 805231320, saletm@engr.colostate.edu
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".