The Mantle Of Eastern Alaska & The Yukon Territory: Analysis Of Global Models & Probabilistic Tomography
Bibliographic record
Abstract
South-Central Alaska possesses a unique corner geometry subduction region that has a rich accretionary history and various slab fragmentation features that are crucial in slab reconstruction models. In this study, a brief overview of seismology/tomography is given, and the major tectonic components of this area are described in their connection to their surrounding tectonic setting. Six global tomography models are examined with respect to the South-Central Alaskan region and compared to assess the existence and extent of tectonic features described in recent seismic studies. The Yakutat slab subducting under the North American continent adjacent to the queen charlotte/Fairweather transform is characterized in terms of its dip, boundaries, interaction with surficial feature (i.e. the Denali Gap, the Pacific Plate, Minto/Fairbanks seismic zones), and potential for fragmentation. An older reconstructed slab associated with the Kula and Farralon plates, the Yukon slab, is also described and searched for in the global models. The global models examined are then compared with much more recent models created with the transdimesional Bayesian inference method that us Reverse-Jump Makarov Monte Carlo iterations to assess uncertainty and error propagation for tomographic models at various depths. The machine learning potential of the TBI method is shown to offer numerous opportunities to further seismological tomographic study and create a new standard for hypothesis testing in plate reconstruction and mantle circulation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".