WallGo investigates: Theoretical uncertainties in the bubble wall velocity
Bibliographic record
Abstract
This record contains the datasets accompanying the publication“Theoretical uncertainties in the bubble wall velocity” (WallGo Collaboration). The data were generated using the WallGo software framework and are used to quantify theoretical uncertainties in state-of-the-art calculations of the bubble wall velocity during first-order cosmological phase transitions. They include numerical results for two extensions of the Standard Mode (SM), the inert doublet model (IDM) and the singlet exteded SM, covering variations in: • the set of particles taken out of equilibrium, • logarithmically and power-enhanced collision integrals, • thermal mass treatments, • nucleation temperatures, • parametrizations of the bubble wall profile (tanh ansatz), and • the perturbative order of the effective potential. Together, these datasets enable the detailed uncertainty budget presented in the associated publication and provide reference results for benchmarking wall velocity and gravitational wave predictions. Whenever applicable, the data files are organized by figure number, model, parameter point, and source of uncertainty e.g. `Fig2Right_IDM_BM1_OutOfEq`.The metadata in the respective files describes the simulation setup, input parameters, and the version of WallGo used for each dataset.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.044 |
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".