Correlation of Venusian Mesoscale Cloud Morphology Between Images Acquired at Various Wavelengths
Bibliographic record
Abstract
Abstract The differences and similarities in morphologies acquired across various wavelengths in simultaneously obtained Venus' dayside images may provide clues on cloud/atmospheric physics and chemistry. Here, we focus on spatial scales smaller than ∼600 km, where cell‐like or streaky features seem to dominate in ultraviolet images. Using images acquired at ultraviolet wavelengths of 283 nm, 365 nm, and infrared wavelengths of 0.90, 2.02, and 10 μm by the Venus orbiter Akatsuki, correlation coefficient maps between pairs of wavelengths, such as 2.02 μm/10 μm, 2.02 μm/283 nm, 2.02 μm/365 nm, 283 nm/365 nm, and 0.90 μm/2.02 μm were created. Our results show a clear negative correlation between images obtained at 2.02 μm (CO2 absorption) and 10 μm (cloud top temperature), meaning that elevated clouds are cooled by adiabatic expansion or the ambient air. A clear negative correlation was found between 2.02 μm and 283 nm (SO2 absorption), suggesting that SO2 is transported to the cloud top region during cloud ascent. We observed a clear positive correlation between images obtained at 283 and 365 nm (unknown absorber), implying a close relationship between the unknown absorber and SO2 or a non‐negligible contribution of the unknown absorber at 283 nm. We found a low correlation between images obtained at 0.90 μm (middle/lower cloud) and 2.02 μm, suggesting a weak vertical coupling in the clouds.
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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 | 0.002 | 0.001 |
| 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 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".