From geological to groundwater flow models: an example of inter-operability for semi-regular grids
Bibliographic record
Abstract
Integrating geologic information into hydrogeologic numerical models is by no means a straightforward operation. This is especially true for finite element modeling which generally requires that the geological information be integrated into grids that are generally irregular in the horizontal (i, j) direction and regular in the vertical (k) direction. Semi-regular grids of this type are still rarely supported by geological modeling packages and the data structure can vary significantly between softwares. Nevertheless, workable solutions exist for some software packages. Here we present a solution that allows the properties of a gOcad geomodel to be transferred to a semiregular grid built in GMS, a commonly used pre-processor for groundwater flow modeling applications. The transfer is achieved first by building in gOcad a "twin grid" that has the same mesh structure as the original GMS grid. This is done using the research plug-in GridLab developed by the gOcad Research Group. Once the properties of the geomodel are transferred to the twin grid, cell or element number correspondences and property transfer to the original GMS grid is achieved using the database system Access. ASCII file format is used for most data exchange between softwares. The time required to go through the procedure is on the order of minutes, even for large grids, making it a practical solution for the lasting problem of property transfer from geological to hydrogeological models, at least until more convenient and built-in interfaces are developed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".