Bibliographic record
Abstract
The N-body problem in k dimensions is the task of determining the time evolution of a system of kN second order ordinary differential equations according to Newtonpsilas inverse square law. It comes up in astrophysics as an approximation to celestial systems. Separately, evolved art is the use of evolutionary computation to create artistic works, visual or otherwise. This study attempts to use the trajectories of 4-rotationally symmetric 2-dimensional N-body initial conditions computed under leapfrog integration as visual art. The integration routine inevitably accumulates roundoff error; the initial conditions are evolved separately to both minimize and maximize the number of timesteps before the system becomes unstable. Unexpectedly, genes evolved to maximize the number of timesteps can reach thousands of times the number from random genes; evolving to minimize creates configurations declared unstable in the first timestep. Visual inspection of the pictures obtained also reveals common motifs among high and low fitness genes: two co-circling planets for high fitness, circling close to the center and being far off for low fitness. Some genes do not follow the motifs and are considered visually appealing by the author. The fitness landscape under this representation is highly multimodal with lots of sharp peaks and troughs, and mostly flat outside.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".