The Role of Surface Processes in Partitioning of Strain and Uplift, St. Elias Orogen, Southern Alaska
Bibliographic record
Abstract
<span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Three-dimensional thermo-mechanical numerical simulations of the ongoing Yakutat–North America collision are used to identify the role of surface processes in triggering localized rapid uplift, exhumation, and strain observed within the St. Elias orogen of southern Alaska. Thermochronological data reveal localized rapid exhumation associated with the Seward-Malaspina and Hubbard Glaciers within a tectonic corner structure where transpressional motion to the south along the Fairweather Fault system transitions to shortening to the north and west within the active fold-and-thrust belt of the St. Elias orogen. The modeled deformation patterns are characteristic of oblique convergence within a tectonic corner, recording the transition from simple shear to contractional strain within a zone spatially consistent with the highest exhumation rates suggesting the corner geometry is the primary control of strain partitioning.</span><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">The relative roles of surface-related processes versus tectonics-related processes in the development of this pattern of deformation were tested with the inclusion of an erosional surface model. The presence of surface processes enhanced the uplift and development of a localized rapid exhumation. When spatially and temporally erosion models are employed, the location of maxima is shifted in response. This indicates that efficient erosion, and resultant deposition and material advection can influence the localization of strain and uplift.</span>
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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 | 0.000 | 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".