Describe and estimate kinetic energy transfers in 3D-turbulent planetary atmospheres
Bibliographic record
Abstract
This talk has been presented at Sorbonne université and Ecole Normale Supérieure de Paris in November 2019 with the following abstract: Images of the giant planets, Jupiter, Saturn and other exoplanets, show highly turbulent storms and swirling clouds that are reflecting the strength of the energetic power enclosed in those planets. Yet, the energetic power of planetary turbulence is inaccessible to conventional tools that require a large amount of data and high resolution two-dimensional maps of the wind field. Here we show that potential vorticity offers a straightforward and universal diagnostic to estimate the global energetic budget in planets. We rely on the conservation of the potential vorticity in stably stratified rotating system to define a length scale, denoted LM , that is the typical distance on which potential vorticity is mixed by planetary turbulence. LM increases with the energetic power of turbulence and can easily be estimated in planets from any instantaneous potential vorticity profile. Our findings lead to the first estimate of Saturn’s energetic power, showing that Saturn’s atmosphere is four times less energetic than the one of Jupiter, consistently with their respective distance to the sun.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".