A new paradigm for reproducing and analyzing N-body simulations of planetary systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The reproducibility of experiments is one of the main principles of the scientific method. However, numerical N-body experiments, especially those of planetary systems, are currently not reproducible. In the most optimistic scenario, they can only be replicated in an approximate or statistical sense. Even if authors share their full source code and initial conditions, differences in compilers, libraries, operating systems or hardware often lead to qualitatively different results. We provide a new set of easy-to-use, open-source tools that address the above issues, allowing for exact (bit-by-bit) reproducibility of N-body experiments. In addition to generating completely reproducible integrations, we show that our framework also offers novel and innovative ways to analyse these simulations. As an example, we present a high-accuracy integration of the Solar system spanning 10 Gyr, requiring several weeks to run on a modern CPU. In our framework, we can not only easily access simulation data at predefined intervals for which we save snapshots, but at any time during the integration. We achieve this by integrating an on-demand reconstructed simulation forward in time from the nearest snapshot. This allows us to extract arbitrary quantities at any point in the saved simulation exactly (bit-by-bit), and within seconds rather than weeks. We believe that the tools we present in this paper offer a new paradigm for how N-body simulations are run, analysed and shared across the community.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it