Rheological bridge zones: the initiation of strain localization
Bibliographic record
Abstract
Strain localization occurs across the crust in both brittle and viscous regimes, but the exact causes remain debated. Natural rock observations suggest that changes in phase properties (such as physical properties, phase distribution, and grain geometry) are more influential in weakening than variations in stress and temperature. Investigating the early stages of strain accumulation in various pressure–temperature conditions leads to a better understanding of these causes. Our study focuses on three weakly deformed rocks showing zones of localization on a millimeter or smaller scale, which we term “bridge zones”. These localized zones appear to mechanically connect weak domains and typically exhibit finer grain sizes within a narrow band. Importantly, these zones occur in less deformed rocks from the margins of shear zones. They result from both in situ grain size reduction and chemical processes leading to phase mixing or element mobility on a limited spatial scale. Numerical modeling aligns high-stress areas with these zones, supporting their impact on reducing rock strength. We propose a conceptual model linking far-field loading to microscale changes in developing these zones. Characterization of bridge zones aids in elucidating the microstructural processes driving deformation localization, which is fundamental for plate tectonics, metamorphism, seismicity, and other lithospheric processes. This research reveals microscale mechanisms driving weak domain development, improving our knowledge of rheological changes and laying the groundwork for predictive models regarding strength evolution in the lithosphere.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".