Permeability Structure of a Strike-Slip Fault
Bibliographic record
Abstract
A study is being conducted on the Wildcat Fault in Berkeley, California, to develop a methodology for characterizing the hydrologic properties of a fault. The geologic setting of the San Francisco Bay Area is intertwined with some of the most complex and active geology in the world. The rocks are extensively sheared and fractured. The Wildcat, a strike-slip fault, appears to consist of multiple fault planes. That the exact location of the main fault is still in dispute among participating researchers highlights the fact that it is very difficult to uniquely characterize such a complex fault zone. The hydrologic characteristics of the Wildcat Fault zone suggest a dual nature, with high permeability along the direction of the fault zone and low permeability across it. Data from cross-hole pumping tests conducted in the high permeability zone along the fault plane exhibit 10:1 near-horizontal anisotropy, which we think is consistent with the fact that the Wildcat is a strike-slip fault. The main philosophy behind our overall approach to the hydrologic characterization of such a complex fractured system is to let the system present its own average property by performing large-scale tests and conducting long-term monitoring, instead of collecting a multitude of data at small length and time scales, or at a discrete fracture scale and to “up-scale,” which is extremely tenuous at best.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".