Bathymetry and absorptivity of Titan's Ontario Lacus
Bibliographic record
Abstract
Ontario Lacus is the largest and best characterized lake in Titan's south polar region. In June and July 2009, the Cassini RADAR acquired its first Synthetic Aperture Radar (SAR) images of the area. Together with closest approach altimetry acquired in December 2008, these observations provide a unique opportunity to study the lake's nearshore bathymetry and complex refractive properties. Average radar backscatter is observed to decrease exponentially with distance from the local shoreline. This behavior is consistent with attenuation through a deepening layer of liquid and, if local topography is known, can be used to derive absorptive dielectric properties. Accordingly, we estimate nearshore topography from a radar altimetry profile that intersects the shoreline on the East and West sides of the lake. We then analyze SAR backscatter in these regions to determine the imaginary component of the liquid's complex index of refraction (κ). The derived value, κ = (6.1−1.3+1.7) × 10−4, corresponds to a loss tangent of tan Δ = (9.2−2.0+2.5) × 10−4 and is consistent with a composition dominated by liquid hydrocarbons. This value can be used to test compositional models once the microwave optical properties of candidate materials have been measured. In areas that do not intersect altimetry profiles, relative slopes can be calculated assuming the index of refraction is constant throughout the liquid. Accordingly, we construct a coarse bathymetry map for the nearshore region by measuring bathymetric slopes for eleven additional areas around the lake. These slopes vary by a factor of ∼5 and correlate well with observed shoreline morphologies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".