Development of New Modeling Tools for Simulating and Designing Reactive Gas Walls
Bibliographic record
Abstract
One of the challenges for multi-species-modeling is the model application to real geological structures at field sites and time scales of interests. In order to study relevant processes at the field scale, the groundwater software package GMS-MODFLOW-RT3D provides all the necessary tools for data processing, data interpolation, creating grid-independent geological structures, and reactive transport modeling at the local scale. We use this modeling strategy for a test site in the Bitterfeld area in eastern Germany, which is highly polluted with chlorinated aliphatics and aromatics (up to 100 ppm). One of the main contaminants is mono-chlorobenzene (MCB). Lab experiments, on-site-, and in-situ reactors show that MCB is not degradable under anaerobic conditions at the test site. Direct gas injection of oxygen could be an alternative. In order to access the complex impact of such remediation option and to optimize the gas injection regime computer models could be helpful. We present a new modeling algorithm, which is based on total concentrations, and which is able to treat a heterogeneous 8-layer model (25000 cells), at a typical time scale, and for a typical species number of interest. The CPU-time on a 2 GHz-Pentium4-machine was about 12 hours for a relevant time scale of 10 years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".