Topographic Diversity in Subduction-Related Mountains Driven by Plate Tectonics and Mantle Dynamics
Bibliographic record
Abstract
Topography at active margins results from complex interactions among plate tectonics, mantle convection, and climate-driven surface processes. However, quantifying the relative contributions of these processes to the diverse topography of active margins remains a significant challenge. Existing geodynamic and surface process models are computationally intensive, often limiting analyses to a narrow set of parameters and regional scales. Here, we introduce a framework that integrates Explainable Artificial Intelligence (XAI) with data from global plate reconstructions, mantle convection, and paleoclimate simulations to identify and quantify the drivers of global active margin topography. Our model predicts present-day elevation with a root mean square error of 444 m and a coefficient of determination (R²) of 0.77 compared to ETOPO. Subduction flux, trench migration rate, and upper mantle temperature are identified as the dominant controls on elevation. These parameters give rise to three distinct topographic regimes: (1) high subduction flux (>0.08 km³/yr) driving elevations above 3000 m (e.g., Central Andes); (2) moderate subduction flux with trench retreat yielding low topography (<1000 m; e.g., Calabria); and (3) advancing trenches producing broad, elevated terrains (>1500 m; e.g., Central Makran). Our framework offers transformative potential for reconstructing the paleotopography of active margins, shedding light on Earth's mineral resource distribution and biodiversity evolution.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".