Fault gouge dating: history and evolution
Bibliographic record
Abstract
ABSTRACT Radiometric dating of fault gouges has become a useful tool for regional tectonics studies and for exploring and understanding fault and earthquake processes. Methods to define the absolute age of faults achieved a solid scientific foundation almost 25 years ago when the development and application of illite age analysis for investigating sedimentary burial and thermal histories found a new potential application – defining the age of fold-and-thrust development. Since then, the methods have benefitted from further development and incorporation of the 40Ar/39Ar micro-encapsulation method and quantitative clay mineral evaluation to distinguish polytypes (Wildfire). These refinements to the methods have improved their application in fold-and-thrust terrains and have opened up applications in normal and strike-slip fault environments. Another important development is the use of absolute dating methods in retrograde clay gouges in which clays in a fault develop from igneous or metamorphic wall rocks that contain no clays. In addition, the method has also been shown to be useful at dating folds in fold-and-thrust belts. We think the method is now an established part of the geological toolkit, look forward to future fault structural and tectonic studies that incorporate fault ages and hope that researchers continue to probe and discover ways that the method can assist fault process studies, including earthquake fault studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".