Multidisciplinary database of permeability of fault zones and surrounding protolith rocks at world-wide sites
Bibliographic record
Abstract
Brittle faults and fault zones are important fluid flow conduits through the upper part of Earth's crust that are involved in many well-known phenomena (e.g. earthquakes, thermal water and gas transport, or water leakage to underground tunnels). The permeability property, or the ability of porous materials to conduct water and gas, is one of the key parameters required in understanding and predicting fluid flow. Although close to a thousand studies have been done, and permeability tested in parts of fault zones, a sytematic summary and database is lacking. This data descriptor is for a multi-disciplinary world-wide compilation and review of bulk and matrix permeability of fault zones: 410 datasets, 521 reviewed sites, 379 locations, >10000 publications searched. The review covers studies of faulting processes, geothermal engineering, radioactive waste repositories, groundwater resources, petroleum reservoirs, and underground engineering projects. The objectives are to stimulate the cross-disciplinary data sharing and communication about fault zone hydrogeology, document the biases and strategies for testing of fault zones, and provide the basic statistics of permeability values for models that require these parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.025 | 0.033 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".