Structural Inheritance Control on Intraplate Present‐Day Deformation: GPS Strain Rate Variations in the Saint Lawrence Valley, Eastern Canada
Bibliographic record
Abstract
Abstract Structural inheritance is one of the key factors commonly proposed to control the localization of strain and seismicity in continental intraplate regions, primarily on the basis of a first‐order spatial correlation between seismicity and inherited tectonic structures. In this paper, we present new GPS (Global Positioning System) velocity and strain rate analyses that provide strong constraints on the magnitude and style of present‐day strain localization associated with the inherited tectonic structures of the Saint Lawrence Valley, eastern Canada. We analyze 143 continuous and campaign GPS stations to calculate velocity and strain rate patterns, with specific emphases on the combination of continuous and campaign velocity uncertainties, and on the definition of robustness categories for the strain rate estimations. Within the structural inheritance area, strain rates are on average 2–11 times higher than surrounding regions and display strong lateral variations of the style of deformation. These GPS velocity and strain rate fields primarily reflect ongoing glacial isostatic adjustment (GIA). Their comparison with GIA model predictions allows us to quantify the impact of the structural inheritance and the associated lithosphere rheology weakening. Outside of the major tectonic inheritance area, GPS and GIA model strain rates agree to first order, both in style and magnitude. In contrast, the Saint Lawrence Valley displays strong strain amplification with GPS strain rates 6–28 times higher than model‐predicted GIA strain rates. Our results provide the first quantitative constraints on the impact of lithospheric‐scale structural inheritance on strain localization in intraplate domains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".