Evaluating the Rheological Controls on Topography Development During Craton Stabilization: Objective Approaches to Comparing Geodynamic Models
Bibliographic record
Abstract
Abstract Surface topography is an important yet largely neglected aspect of the early evolution of cratons. The lateral accretion of cratonic nuclei inevitably forms orogenic belts that subsequently provide a sediment source for large, resource‐rich intracratonic basins, but to date, geodynamic models have focused exclusively on lithospheric root processes. Here we use two‐dimensional thermal‐mechanical models to study the topography and lithospheric deformation during 50 Myr of compression of a cratonic nucleus, to simulate the lateral accretion phase of craton growth in the Neoarchean. Although the cratonic nucleus thickens slightly during the compression phase, most of the deformation occurs in the regions adjacent to the nucleus that have weaker lithosphere. Here, crustal thickness triples developing high topography in excess of 10 km without active erosion. Models with different initial rheological parameters will have different final topography and lithosphere geometry, but in general it is difficult to shorten and deform the depleted cratonic nucleus, unless there are significantly weak heterogeneities in the mantle lithosphere. We apply two quantitative analysis techniques to objectively evaluate a multitude of model outputs. Cross‐correlation clustering (CCC) measures the degree of similarity between topography profiles and categorizes models based on the general topographic character. Six different topography families are possible in the context of our models and crustal strength is the most important parameter affecting the shape. From principal component analysis (PCA) we identify four dominant lithosphere geometries. When used together, these two methods provide distinct yet complementary information about the surface and subsurface deformation features in our models.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".