Analyzing the horizontal orientation of the crustal stress adjacent to plate boundaries
Bibliographic record
Abstract
The spatial analysis of horizontal stress orientation is important to study stress sources and understand tectonics and the deformation of the lithosphere. Additional to the stress sources, the geometry of stress fields depends on the underlying coordinate reference system, which causes spatial distortions that bias the analysis and interpretation of stresses. The bias can be avoided when the stress field is decomposed and transformed into the reference frame of its first-order stress source. We present a modified and extended theory based on the empirical link between the orientation of first-order stresses and the trajectories of lateral plate boundary forces. This link is applied to analyze the orientation of horizontal stresses, their patterns, and tectonic structures from the perspective of their first-order source or cause. By using only parameters for the relative motion between two neighboring plates, we model the first-order orientation of the maximum horizontal stress that statistically fits the orientation of [Formula: see text]80% of the global stress data adjacent to plate boundaries. Considerable deviations of the observed stress from the predicted first-order stress direction can reveal the geometry of second-order stresses and confine areas where other stress sources dominate. The model's simple assumptions, independence from the sample size, potential application to regional to global scale analysis, and compatibility with other spatial interpolation algorithms make it a powerful method for analyzing stress fields. For immediate use, the presented method is implemented in the free and open-source software package tectonicr, which is written in the computer language R.
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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 | 0.001 |
| 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) | 0.002 | 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".