The biases and trends in fault zone hydrogeology conceptual models: global compilation and categorical data analysis
Bibliographic record
Abstract
Abstract To investigate the biases and trends in observations of the permeability structures of fault zones in various geoscience disciplines, we review and compile a database of published studies and reports containing more than 900 references. The global data are categorized, mapped, and described statistically. We use the chi‐square test for the dependency of categorical variables to show that the simplified fault permeability structure (barrier, conduit, barrier–conduit) depends on the observation method, geoscience discipline, and lithology. In the crystalline rocks, the in situ test methods (boreholes or tunnels) favor the detection of permeable fault conduits, in contrast to the outcrop‐based measurements that favor a combined barrier–conduit conceptual models. These differences also occur, to a lesser extent, in sedimentary rocks. We provide an estimate of the occurrence of fault conduits and barriers in the brittle crust. Faults behave as conduits at 70% of sites, regardless of their barrier behavior that may also occur. Faults behave as barriers at at least 50% of the sites, in addition to often being conduits. Our review of published data from long tunnels suggests that in crystalline rocks, 40–80% (median about 60%) of faults are highly permeable conduits, and 30–70% in sedimentary rocks. The trends with depth are not clear, but there are less fault conduits counted in tunnels at the shallowest depths. The barrier hydraulic behavior of faults is more uncertain and difficult to observe than the conduit.
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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 | 0.063 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.021 | 0.031 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".