Changes in magnetic and fractal properties of fractured granites near the Nojima Fault, Japan
Bibliographic record
Abstract
Abstract Anisotropy of magnetic susceptibility (AMS) has been used to infer finite strain fabrics in plastically deformed rocks, but there are few studies of magnetic properties in fractured fault rocks. Changes in magnetic and fractal properties of fractured granites from the Disaster Prevention Research Institute, Kyoto University (DPRI) 500 m drilling core towards the Nojima Fault and of the well‐foliated fault gouge are described. Fractal analysis of fractured granites shows that the fractal dimension (D) increases linearly toward the gouge zone of the fault. In weakly fractured granites (D = 1.05–1.24), it was found that the degree of AMS correlates positively with the fractal dimension, suggesting a fracture‐related magnetic fabric due to fracturing. In strongly fractured granites (D = 1.25–1.50), weaker, nearly isotropic AMS is found, suggesting erasure by the fragmentation of the magnetic minerals. Within the fault gouge zone, an isotropic AMS fabric was found, as well as twofold increases in magnetic intensity and susceptibility. These changes reflect the production of new magnetite grains, subsequently confirmed by hysteresis studies, which suggests that fault gouge might be regarded as the source of the regional geomagnetic field contrast along active faults. Thus, AMS is clearly a potentially useful tool for inferring the fracturing texture of magnetic minerals in fractured rocks and detecting active faults from the high susceptibility contrast of fault gouge.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".