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Record W4392644853 · doi:10.5194/egusphere-egu24-20474

CAMEMBERT: A Mini-Neptunes General Circulation Model Intercomparison

2024· preprint· en· W4392644853 on OpenAlexaff
Duncan Christie, Aaron David Schneider, Benjamin Charnay, Denis E. Sergeev, Elspeth K. H. Lee, Emily Rauscher, Hamish Innes, Isaac Malsky, L. Carone, Maria E. Steinrueck, Maria Zamyatina, Michael T. Roman, Nathan J. Mayne, Pascal A. Noti, Russell Deitrick, Thomas J. Fauchez, Thaddeus D. Komacek, J. Chen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicEnergy Load and Power Forecasting
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCirculation (fluid dynamics)General Circulation ModelEnvironmental scienceClimatologyPhysicsGeologyOceanographyMechanicsClimate change

Abstract

fetched live from OpenAlex

With observatories such as JWST, astronomers are now attempting to better understand the previously inscrutable atmospheres of mini-Neptunes, the smaller and often cooler cousins of the better-studied hot Jupiters. General circulation models (GCMs) are an essential part of the toolset used to improve that understanding, from both the perspective of theoretical investigations and in support of observation. It is only recently, however, that the exoplanet community has begun to benchmark and study the behaviour of our GCMs through intercomparisons, a practice that has been going on in the Earth sciences community for decades. With that in mind, we present CAMEMBERT (Comparing Atmospheric Models of Extrasolar Mini-Neptunes Building and Envisioning Retrievals and Transits), a community effort to compare the many of the GCMs used in studies of mini-Neptunes using common parameters and targets in hopes to better understand differences between our models and to better inform the interpretations of our results. This talk will cover the protocol as well as preliminary insights from the project. CAMEMBERT is a part of the CUISINES meta-framework, which also includes the THAI, SAMOSA, CREME, and MOCHA GCM intercomparisons.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.250
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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