The effective elastic thickness of the continental lithosphere: Comparison between rheological and inverse approaches
Bibliographic record
Abstract
Following the release of global continental effective elastic thickness (Te) maps obtained using different approaches, we now have the opportunity to provide better constraints onTe.We improve previous estimates ofTederived from thermo‐rheological models of lithospheric strength (orTer) using new equations that consider variations of the Young's Modulus in the lithosphere. These new values are quantitatively compared with those obtained from an inverse approach (orTei) based on a comparison of the spectral coherence between topography and gravity anomalies with the flexural response of an equivalent elastic plate to loading. The two models show in general a good agreement, having equal means (at the 95% significance level) in about half of the continental areas. In other regionsTeiexceedsTerin about 65% of the data points, showing thatTeiprovides an upper bound onTe.The two data sets have a similar range, but demonstrate different distributions.Terhas a bimodal distribution, with the two peaks representative of the cratons and of the areas outside of them. In contrast,Teihas more uniform distribution without predominant peaks. Our models show higher similarities in the Meso‐Cenozoic orogens than in the Archaean and Proterozoic shields and platforms, due to the methods employed. For the regions with the most robust determinations ofTerandTei, the relationship between them is close to linear. The results of this work can be used for further studies on the mechanical properties of the lithosphere.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".