A systematic study of CO2 planetary atmospheres and their link to the stellar environment
Bibliographic record
Abstract
ABSTRACT The Milky Way Galaxy is literally teeming with exoplanets; thousands of planets have been discovered, with thousands more planet candidates identified. Terrestrial-like planets are quite common around other stars, and are expected to be detected in large numbers in the future. Such planets are the primary targets in the search for potentially habitable conditions outside the Solar system. Determining the atmospheric composition of exoplanets is mandatory to understand their origin and evolution, as atmospheric processes play crucial roles in many aspects of planetary architecture. In this work we construct and exploit a 1D radiative transfer model based on the discrete-ordinates method in plane-parallel geometry. Radiative results are linked to a convective flux that redistributes energy at any altitude producing atmospheric profiles in radiative–convective equilibrium. The model has been applied to a large number (6250) of closely dry synthetic CO2 atmospheres, and the resulting pressure and thermal profiles have been interpreted in terms of parameter variability. Although less accurate than 3D general circulation models, not properly accounting for e.g. clouds and atmospheric and ocean dynamics, 1D descriptions are computationally inexpensive and retain significant value by allowing multidimensional parameter sweeps with relative ease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".