The Arctic Region Lithosphere: Thermal, strength and effective elastic thickness model
Bibliographic record
Abstract
I estimate for the first time the lithospheric thermal, strength and and effective elastic thickness (Te) distribution in the entire Arctic region north of 68 ° latitude. To this aim, I use the most recently updated models of the Arctics crust of Lebedeva-Ivanova et al. (in reparation). These models include the thickness and density of the crust and sediments, the boundaries between the continental and oceanic crust, and the age of the oceanic lithosphere. I estimate the temperature variation in the continental lithosphere by using the\none-dimensional steady-state heat conductive equation, selectively combining the models for a constant surface heat flow of 50 and 62 mW/m2 and assuming a ratio between the upper and lower crust of 0.5 and 0.7, respectively. I adopt the global depth and heat flow model (GDH1) of Stein and Stein (1992) to estimate the temperature in the oceanic domain. The thermal models are used as input for estimating the integrated strength and the Te for a 'hard' and 'soft' rheology. This study finds that the new temperature models clearly reveal the cold cratonic areas and the hotter areas within the continental shelves and around the Mid-Atlantic ridge. This consequently results in higher integrated strength corresponding to the cratonic areas and lower integrated strengths, corresponding to tectonic active areas. Comparing the intraplate earthquake distribution with the strength and Te and brittle-ductile transition variations indicates that the highest seismicity events occur within areas of weak and thin lithosphere (Mid-Atlantic Ridge, Laptev Sea) and at the zones of strong lateral change in strength and Te (edges of Greenland and Canadian Artic) which is in accordance with expectations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".