Allocating transmissivities from constant head tests for the development of DFN models
Bibliographic record
Abstract
Constant head tests are commonly used for field measurements of fracture transmissivity. As a bulk transmissivity is measured for each test section, it is frequently unclear how this transmissivity relates to the hydraulic properties of individual fractures. The goal of this study is to determine if constant head tests conducted at scales larger than the average fracture spacing can be used to generate discrete fracture network (DFN) models that describe transport. The methodology involved generating DFNs using measurements from constant head tests conducted at lengths both above and below the average fracture spacing at a site in Ontario, Canada. Transport predictions from DFNs produced from different scales of hydraulic tests were compared to determine if a method for proportioning larger-scale test results to obtain a DFN representative of the smaller-scale tests could be established. The results of this study indicate that the choice of method used to apportion bulk transmissivities has a significant impact on transport simulations, with a difference in mass arrival of over a factor of two at travel distances less than 50 m. While the most appropriate method is case specific, the error resulting from the choice needs to be considered when using DFN models.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".