Segmentation of Slow Slip Events in South Central Alaska Possibly Controlled by a Subducted Oceanic Plateau
Bibliographic record
Abstract
Abstract Recent GPS observations show that slow slip events in south central Alaska are segmented along strike. Here we review several mechanisms that might contribute to this segmentation and focus on two: along‐strike variation of slab geometry and effective normal stress. We then test them by running numerical simulations in the framework of rate‐and‐state friction with a nonplanar fault geometry. Results show that the segmentation is most likely related to the along‐strike variation of the effective normal stress on the fault plane caused by the Yakutat Plateau. The Yakutat Plateau could affect the effective normal stress by either lowering the pore pressure in Upper Cook Inlet due to less fluids release or increasing the normal stress due to the extra buoyancy caused by the subducted Yakutat Plateau. We prefer the latter explanation because it is consistent with the relative amplitudes of the effective normal stress in Upper and Lower Cook Inlet and there is very little along‐strike variation in Vp/Vs ratio in the fault zone from receiver function analysis. However, we cannot exclude the possibility that the difference in effective normal stress results from along‐strike variation of pore pressure due to the uncertainties in the Vp/Vs estimates. Our work implies that a structural anomaly can have a long‐lived effect on the subduction zone slip behavior and might be a driving factor on along‐strike segmentation of slow slip events.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".