Mixed discrete-continuum models: A summary of experiences in test interpretation and model prediction
Bibliographic record
Abstract
A number of conceptual models have been proposed for simulating groundwater flow and solute transport in fractured systems. They span the range from continuum porous equivalents to discrete channel networks. The objective of this paper is to show the application of an intermediate approach (mixed discrete-continuum models) to three cases. The approach consists of identifying the dominant fractures (i.e., those carrying most of the flow) and modeling them explicitly as two-dimensional features embedded in a three-dimensional continuum representing the remaining fracture network. The method is based on the observation that most of the water flows through a few fractures, so that explicitly modeling them should help in properly accounting for a large portion of the total water flow. The applicability of the concept is tested in three cases. The first one refers to the Chalk River Block (Canada) in which a model calibrated against a long crosshole test successfully predicted the response to other tests performed in different fractures. The second case refers to hydraulic characterization of a large-scale (about 2 km) site at El Cabril (Spain). A model calibrated against long records (five years) of natural head fluctuations could be used to predict a one-month-long hydraulic test and heads variations after construction of a waste disposal site. The last case refers to hydraulic characterization performed at the Grimsel Test Site in the context of the Full-scale Engineered Barrier EXperiment (FEBEX). Extensive borehole and geologic mapping data were used to build a model that was calibrated against five cross-hole tests. The resulting large-scale model predicted steady-state heads and inflows into the test tunnel. The conclusion is that, in all cases, the difficulties associated with the mixed discrete-continuum approach could be overcome and that the resulting models displayed some predictive capabilities.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".