Venus as a Laboratory for Exoplanetary Science
Bibliographic record
Abstract
The current goals of the astrobiology community are focused on developing a framework for the detection of biosignatures, or evidence thereof, on objects inside and outside of our solar system. A fundamental aspect of understanding the limits of habitable environments (surface liquid water) and detectable signatures thereof is the study of where the boundaries of such environments can occur. Such studies provide the basis for understanding how a once inhabitable planet might come to be uninhabitable. The archetype of such a planet is arguably Earth's sibling planet, Venus. Given the need to define the conditions that can rule out biorelated signatures of exoplanets, Venus provides a unique opportunity to explore the processes that led to a completely uninhabitable environment by our current definition of the term. Here we review the current state of knowledge regarding Venus, particularly in the context of remote sensing techniques that are being or will be employed in the search for and characterization of exoplanets. We discuss candidate Venus analogs identified by the Kepler and TESS exoplanet missions and provide an update to exoplanet demographics that can be placed in the potential runaway greenhouse regime where Venus analogs are thought to reside. We list several major outstanding questions regarding the Venus environment and the relevance of those questions to understanding the atmospheres and interior structure of exoplanets. Finally, we outline the path towards a deeper analysis of our sibling planet and the synergy to exoplanetary science.
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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 | 0.002 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".