Database of Accreting Binary Simulations
Bibliographic record
Abstract
We present DABS (Database of Accreting Binary Simulations), an open-access database of modelled Low Mass X-ray Binaries (LMXBs). DABS has been created using evolutionary tracks of neutron star and black hole LMXBs, spanning a large set of initial conditions for the accretor mass, donor mass, and orbital period. The LMXBs are evolved with the Convection and Rotation Boosted Magnetic Braking prescription. The most important asset of this online database is the tool PEAS (Progenitor Extractor for Accreting Systems) https://github.com/ChatrikMangat/progenlmxb. This tool can be used to predict the progenitors of any user-entered LMXB system and view their properties before the start of mass transfer. This prediction can facilitate preliminary searches for the progenitors of observed LMXBs, which can help in streamlining further detailed analyses. The PEAS tool can also be used to constrain population synthesis techniques that specialize in supernova kicks in binaries and common envelope outcomes.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.022 |
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".