World strength map: preliminary results
Bibliographic record
Abstract
Rheology and strength of the Earth’s lithosphere have been debated since the beginning of the last century, when the concept of a strong lithosphere overlying viscous asthenosphere was introduced. The issue of strength of the lithospheric plates and their spatial and temporal variations is important for many geodynamic applications. For rocks with given mineralogical composition and microstructure, temperature is one of the most important parameters controlling rheology.We present the first world strength map obtained from global thermal and crustal models. Temperature estimates for the deeper horizons of the lithosphere, where the heat transport is mostly conductive, requires a precise knowledge of many crustal parameters (mainly thermal conductivity and heat production), which are extremely uncertain. Therefore, we use a combination of indirect approaches, such as seismic tomography and geothermal analysis. Furthermore, we implement a global crustal model on the base of previous compilations. Lithology of the upper and lower crust was classified based on tectonic maps of the World in agreement with the previous study of Tesauro et al. (2009). The results show a good correspondence between strength values and geological features. We observe some general tendency for old cratons and areas affected by the Tertiary volcanism, characterized by high and low strength values, respectively. At the same time, relevant differences in the strength distribution between similar structures are found.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.023 |
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".