Bibliographic record
Abstract
See https://github.com/sympy/sympy/wiki/release-notes-for-1.9 for the release notes. Filename Description size sha256 sympy-1.9.tar.gz The SymPy source installer. 6.6M c7a880e229df96759f955d4f3970d4cabce79f60f5b18830c08b90ce77cd5fdc sympy-1.9-py3-none-any.whl A wheel of the package. 5.9M 8bc5de4608b7aa4e7ffd1b25452ae87ccc5f6ca667c661aafb854a1ade337d0c sympy-docs-html-1.9.zip Html documentation. This is the same as the online documentation. 30M 3ca9e0a07e0eb2c8d9a39d640a486060b154a4866eba6e051b5add6a31b34f95 sympy-docs-pdf-1.9.pdf Pdf version of the html documentation. 12M 2527db3dc4e58f74e8eda65d2e2dba9de8c1a7fc8d6a2326227d4346ca71a3ab Note: Do not download the Source code (zip) or the Source code (tar.gz) files below.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.740 | 0.840 |
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".