Bibliographic record
Abstract
:rocket: New features Added fully automated deployment to PyPI via GitHub Actions Added dns submodule with tortuosity function Added customized bar plot option to visualization module Added full pytest integration for automated testing of notebook examples Added custom imshow and a wrapper at top level for instant access Added GitHub Actions workflow to replace Travis CI Added two functions to tools module: size_to_seq and seq_to_satn Added two functions to tools module: size_to_seq and seq_to_satn Added option to trim_nonpercolating_paths to accepts masks Added porosity_profile example :warning: API changes Changed direction to axis in some vis functions Removed dims and ndims, typos Changed all internal distance transforms to much faster edt instead of scipy.ndimage.distance_transform_edt :bug: Bugfixes Fixed CI testing error due to pep8 incompatibility with the latest pytest Fixed mising dns submodue install in setup file Fixed subdivide to return tuples, enhanced to accept flattened and overlap args Fixed bugs in snow_n regarding non-contiguous aliases and missing labels Fixed cylinders generator to provide a more uniform porosity distribution Fixed some examples that were issuing deprecation warnings
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.289 | 0.239 |
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".