Bibliographic record
Abstract
PyAutoFit: Nautilus now outputs results on the fly: https://github.com/rhayes777/PyAutoFit/pull/961 Output latent samples of a model-fit, which are parameters derived from a model which may be marginalized over: PR: https://github.com/rhayes777/PyAutoFit/pull/994 Example: https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/cookbooks/analysis.ipynb model.info file displays complex models in a more concise and readable way: https://github.com/rhayes777/PyAutoFit/pull/1012 All samples with a weight below an input value are now removed from samples.csv to save hard disk space: https://github.com/rhayes777/PyAutoFit/pull/979 Documentation describing autofit scientific workflow: https://github.com/rhayes777/PyAutoFit/pull/1011 Refactor visualization into stand alone module: https://github.com/rhayes777/PyAutoFit/pull/995 Refactor how results are returned after a search: https://github.com/rhayes777/PyAutoFit/pull/989 Improved parallelism logging: https://github.com/rhayes777/PyAutoFit/pull/1009 Likelihood consistency check now performed internally: https://github.com/rhayes777/PyAutoFit/pull/987 Generation of initial search samples is now performed in parallel: https://github.com/rhayes777/PyAutoFit/pull/997 No longer store search_internal on hard-disk. simplifying source code internals: https://github.com/rhayes777/PyAutoFit/pull/938 Multiple small bug fixes and improvements to interface. PyAutoGalaxy: Remove Plane object and replace with Galaxies object Shapelets improvements: https://github.com/Jammy2211/PyAutoGalaxy/pull/173 Adaptive over sampling of grids for a pixelization: https://github.com/Jammy2211/PyAutoGalaxy/pull/168 BasisPlotter which plots each basis (e.g. each Gaussian of an MGE): https://github.com/Jammy2211/PyAutoGalaxy/pull/173 Plot mappings between source and image plane of a pixelization as lines: https://github.com/Jammy2211/PyAutoGalaxy/pull/172 For multi-wavelength datasets model offsets between each dataset: https://github.com/Jammy2211/PyAutoGalaxy/pull/171 Modeling of background sky: https://github.com/Jammy2211/PyAutoGalaxy/pull/170 Improvements to use of adapt images for adaptive pixelizations: https://github.com/Jammy2211/PyAutoGalaxy/pull/160 Improved angle conversions for computing errors on mass profile and shear angles from ell_comps: https://github.com/Jammy2211/PyAutoGalaxy/pull/169 Remove sub_size from all classes (e.g. Array2D, Mask2D) to simplify API. MaternKernel added: https://github.com/Jammy2211/PyAutoGalaxy/pull/148
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.771 | 0.783 |
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".