Modelisation of tidal flows between Titan’s seas Kraken Mare and Ligeia Mare
Bibliographic record
Abstract
Numerous lakes and seas filled with liquid hydrocarbons have been detected on Titan's surface by Cassini spacecraft [1]. Most of these liquid bodies are located in the northern high latitudes [2]. In this study, we focus on two of them: Kraken Mare and Ligeia Mare and in particular on the tidal currents between them. Recent observations of the Visual and Infrared Mapping Spectrometer (VIMS) from February 12, 2015 suggested the presence of waves in a strait called Trevize fretum linking the two seas [3]. These waves could be generated by either wind or strong currents between Kraken Mare and Ligeia Mare. We simulate the tidal response of Kraken Mare and Ligeia Mare and the currents in the straits linking those seas with SLIM (Second-generation Louvain-la-Neuve Ice-ocean Model, www.climate.be/slim). SLIM resolves 2D shallow water equations on an unstructured mesh, which allows higher accuracy in the straits without drastically increasing the computational costs. It has been recently used to simulate the tidal response in Ontario Lacus [4]. The tide generating force modeled in this work is the gradient of tidal potential due to Titan's obliquity and Titan's orbital eccentricity around Saturn (other contribution such as sun tide generating force are unheeded). Kraken Mare and Ligeia Mare composition might be different. Consequently, fluid exchanges could also occur due to a density gradient between the two seas. In this study, we focus on the flow in the strait between Kraken Mare and Ligeia Mare and consider the effect of parameters such as the composition, solid deformations and the depth of the strait. [1] Stofan et al. (2007) Nature, 445, 61-64.[2] Aharonson et al. (2009), Nature geoscience, 2(12), 851-854. [3] Sotin et al. (2015) AGU, P12B-04. [4] Vincent et al. (2016) Ocean Dynamics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".