The CUISINES 2024 menu: Updates and progress on a large exoplanet model intercomparison framework
Bibliographic record
Abstract
The exoplanet community possesses an incredible variety of models to match the astronomical diversity of exoplanets. Those models are used to either predict or interpret exoplanet data. However, contrary to Earth science, we have no existing ground truth to validate those models. Meanwhile, we would still learn a lot from benchmarking exoplanet models together to increase the robustness in our data prediction and interpretation, to identify bugs and to highlight model features that would require additional developments.The Climates Using Interactive Suites of Intercomparisons Nested for Exoplanet Studies (CUISINES) Working Group of NASA’s Nexus for Exoplanet Systems Science (NExSS) supports a systematized approach to evaluating the performance of exoplanet models, and provides here a framework for conducting community-organized exoplanet Model Intercomparison Projects (exoMIPs). The CUISINES framework adapts Earth climate community practices specifically for the needs of the exoplanet researchers, encompassing a range of model types, planetary targets, and parameter space studies. In this presentation, we will give updates on the various exoMIPs and we will provide insights on our findings to make an exoplanet model intercomparison a success on short and long timescales.
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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 | 0.035 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.094 | 0.047 |
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".