ExoPlaSim models for "Climate uncertainties caused by unknown land distribution on habitable M-Earths"
Bibliographic record
Abstract
Files for the simulations in the paper "Climate uncertainties caused by unknown land distribution on habitable M-Earths." .nc files are the model outputs, .cfg files are ExoPlaSim configuration files that can be used to rerun our simulations, and .sra files are the landmaps used in the simulations. Outputs and landmaps are at T63 resolution unless otherwise stated, and are named according to the landmap class: SC for SubCont, SO for SubOcean, and RC for RandCont. The decimal number following the landmap class is the planet's land fraction on the dayside, such that, for example, a file beginning with SC-0.30 has a substellar continent occupying 60% of the dayside. The next part of the names refers to the simulation parameters: default (default parameters in the paper), S0-700 (instellation of 700W/m^2), Ts-2600 or Ts-3500 (stellar temperature of 2600 or 3500K), or pco2-100000 (0.1 bar of CO2). Subsequent characters represent iterations of models that previously crashed and do not affect the model setup. RC models all have default parameters. Letters are used to distinguish between RC models with the same land fraction but different landmaps.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.067 |
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".