Bibliographic record
Abstract
Major API changes and new features! If you have used a previous version of REBOUND, then you will need to update your code. If you have trouble with the migration, open a GitHub issue! Many function and variable names have changed. They now follow a coherent naming convention. See the naming convention section in the documentation for more information. New visualization module! Previously, using OpenGL visualization required the GLFW library which led to problems on various operating systems. The new visualization module no longer requires ANY dependencies and is compatible with MacOS, Linux, and Windows. It works by running a local web server to which you can point your browser to. In your web browser, an emscripten compiled version of REBOUND handles the WebGL visualization while constantly updating simulation data over HTTP. You can use ssh and port forwarding to visualize simulations on remote servers. Check out the documentation for more details on this new module. OpenGL for all the examples has been turned off by default so that new users don't get stuck at this step. To turn on OPENGL simply change the flag in the Makefile. Added emscripten support. All C examples (including those using visualizations) are now automatically compiled with emscripten on readthedocs.org so you can run from within the browser. No download or installation required. A race condition in OpenGL visualization has been removed. Visualizations run much smoother. reb_random functions now callable with r=NULL. If r=NULL then the time and PID is used as a seed. Removed support for Simulationarchives with version 2. Added some additional support for reading corrupt/old archives. Fixed memory leak in reb_simulation_copy. Consistent integer sizes for 32/64bit. This includes padding for reb_particle which is stored in the Simulationarchive. Version 3.x
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.442 | 0.473 |
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".