Bibliographic record
Abstract
Changelog included below: New Features Added a .replicate() method to Potential classes to enable copying potential objects but modifying some parameter values. Added a new potential class MN3ExponentialDiskPotential based on Smith et al. (2015): an approximation of the potential generated by a double exponential disk using a sum of three Miyamoto-Nagai disks. The Orbit.estimate_period() method now returns period estimates in all phase-space components instead of just the radial period. Added a store_all flag to the integrators to control whether to save phase-space information for all timesteps or only the final timestep. Added a plot_rotation_curve() method to all potential objects to make a 1D plot of the circular velocity curve. Added a new potential for representing multipole expansions MultipolePotential. Added a new potential CylSplinePotential for flexible representation of axisymmetric potentials by allowing passing in grids of potential values evaluated grids of R, z values (like the CylSpline potential in Agama). Added a show_time flag to Orbit.animate() to control whether to show the current timestep. Changed Orbit.animate() to allow for different marker_style and segment_style options for individual orbits by passing a list of dicts instead of just a dict. Added an experimental new class SCFInterpolatedPotential that accepts a time series of coefficients and interpolates the coefficient values to any evaluation time. Bug fixes Fixed a bug where the NFWPotential energy was nan when evaluating at the origin, and added tests for all potentials to check for a finite value of the potential at the origin (when expected). Fixed a bug in NFWPotential.from_M200_c() where the incorrect scale radius was computed (Cython does not always use Python 3 division rules for dividing integers!). Fixed a bug in the (C-level/internal) estimation of the 2nd derivative of the potential, used to generate mock streams, that affects non-conservative force fields. API changes The Orbit.estimate_period() method now returns period estimates in all phase-space components instead of just the radial period.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.452 | 0.658 |
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".