Bibliographic record
Abstract
Version 4.11.7 Support for Python 3.12 using the Meson build system This version no longer relies on distutils (which was deprecated in python 3.12) and instead makes use of Meson and Meson-Python to build and test the pyshtools package. The package can be built from source using pip as before, however, if you need to create an editable install, it will be necessary to use the slightly modified command pip install --no-build-isolation -e . Please see the online documentation for instructions on how to run the test suites and benchmarks. Other changes We no longer use versioneer to determine the package version, but instead set the version in the main meson.build file using setuptools_scm. At the present time, it is not possible to determine the version when using a source tarball, and for this case, the build will fail. Please ensure that when building from source that you are doing so from a git versioned repository. Fixed a problem with SHCoeffs.volume() when the coefficient normalization was ortho. Fixed a potential LAPACK underscore problem when compiling with LAPACK_UNDERSCORE specified. Minor changes were made to the python source files to ensure numpy v2 compatibility. M. A. Wieczorek, M. Meschede, A. Broquet, T. Brugere, A. Corbin, EricAtORS, A. Hattori, A. Kalinin, J. Kohler, D. Kutra, K. Leinweber, P. Lobo, I. Oshchepkov, P.-L. Phan, O. Poplawski, M. Reinecke, E. Sales de Andrade, E. Schnetter, S. Schröder, J. Sierra, A. Vasishta, A. Walker, xoviat, B. Xu (2024). SHTOOLS: Version 4.11.7, Zenodo, doi:10.5281/zenodo.592762
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.292 | 0.416 |
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".