Blue Marble, Stagnant Lid: Could Dynamic Topography Avert a Waterworld?
Bibliographic record
Abstract
Abstract Topography on a wet rocky exoplanet could raise land above its sea level. Although land elevation is the product of many complex processes, the large-scale topographic features on any geodynamically active planet are the expression of the convecting mantle beneath the surface. This so-called “dynamic topography” exists regardless of a planet’s tectonic regime or volcanism; its amplitude, with a few assumptions, can be estimated via numerical simulations of convection as a function of the mantle Rayleigh number. We develop new scaling relationships for dynamic topography on stagnant lid planets using 2D convection models with temperature-dependent viscosity. These scalings are applied to 1D thermal history models to explore how dynamic topography varies with exoplanetary observables over a wide parameter space. Dynamic topography amplitudes are converted to an ocean basin capacity, the minimum water volume required to flood the entire surface. Basin capacity increases less steeply with planet mass than does the amount of water itself, assuming a water inventory that is a constant planetary mass fraction. We find that dynamically supported topography alone could be sufficient to maintain subaerial land on Earth-size stagnant lid planets with surface water inventories of up to approximately 10−4 times their mass, in the most favorable thermal states. By considering only dynamic topography, which has ∼1 km amplitudes on Earth, these results represent a lower limit to the true ocean basin capacity. Our work indicates that deterministic geophysical modeling could inform the variability of land propensity on low-mass planets.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".