Estimates of fluvial erosion on Titan from sinuosity of lake shorelines
Bibliographic record
Abstract
Titan has few impact craters, suggesting that its surface is geologically young. Titan's surface also has abundant landforms interpreted to be fluvial networks. Here we evaluate whether fluvial erosion has caused significant resurfacing by estimating the cumulative erosion around the margins of polar lakes. A scarcity of detailed topographic data makes it difficult to measure fluvial incision on Titan, but images of drowned fluvial features around the lake margins, where elevated levels of hydrocarbon liquids appear to have partly flooded fluvial valleys, offer a constraint on the topography. We mapped the shorelines of several lakes to obtain topographic contours that trace the fluvially dissected topography. We then used a numerical landscape evolution model to calibrate a relationship between contour sinuosity, which reflects the extent of fluvial valley incision, and cumulative erosion. We confirmed this relationship by analyzing a partially dissected surface adjacent to the Minnesota River, USA. Comparison of the mapped Titan contours with the sinuosity‐erosion relationship suggests that cumulative fluvial erosion around the margins of Titan's polar lakes, including Ligeia Mare, Kraken Mare, and Punga Mare in the north and Ontario Lacus in the south, ranges from 4% to 31% of the initial relief. Additional model simulations show that this amount of fluvial erosion does not render craters invisible at the resolution of currently available imagery, suggesting that fluvial erosion is not the only major resurfacing mechanism operating in Titan's polar regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".