Bibliographic record
Abstract
First stable version of pyFAI: v2023.1. Sources and binary wheels can be found at: https://pypi.org/project/pyFAI/ and at: https://github.com/silx-kit/pyFAI/releases/tag/v2023.1 One of a few ways to install this release with pip: pip install pyFAI==2023.1.0 Release notes and important changes since 0.21: Developer and packager tools: Switch build system from numpy.distutils to meson-python Keep the former setup.py for compatibility reasons: it will be removed in a future release Drop Python 3.6 (default parameters in namedtuple feature used) Require silx 1.1 (for OpenCL), scipy and matplotlib GUI side: several minor improvements in pyFAI-calib2 Fixed calibration in jupyter-lab Core improvements: Refactoring of the Geometry class Geometry pseudo-inversion optimization Improved support from Medipix-based Lambda-detectors New detectors from Dectris (Pilatus 900k and Eiger 250k) Support Nexus format in output: NXmonpd and NXcansas Single-threaded CSC sparse matrix multiplication engine Improved uncertainty propagation: Refactor error model management (uses enum) Hybrid error model (azimuthal for sigma-clipping but reports Poissonian noise) Export peakfinder data to the CXI format (used by CrystFEL) Improvement in the doc: Update installation instructions Multi-threaded integration tutorial GPU implementation tutorial Facts and figures: 400+ commits, 100 PR +with the contribution of: Clemens Prescher, Elena Pascal, Jérôme Kieffer, Malte Storm, Marco Cammarata, Michael Hudson-Doyle, Picca Frédéric-Emmanuel, Rodrigo Telles, Thomas A Caswell, Tommaso Vinci, Valentin Valls, Wout de Nolf.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.363 | 0.522 |
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".