SystemsBioinformatics/cbmpy: CBMPy release 0.8.8
Bibliographic record
Abstract
Release 0.8.8 Intermediate release that includes bug fixes, code cleanup and new features to support endPoint FBA. Has alpha support for Flux Balance Constraints V3 features including quadratic objectives, non-stoichiometric user constraints and KeyValue pairs. Try the latest format with cbmpy.writeSBML3FBCV3. Many thanks to Steven Wijnen for his debugging, testing and help with the QP, constraints and FBCV3 implementation. This release supports his dcFBA/endPointFBA https://github.com/SystemsBioinformatics/dynamic-community-fba package . See https://systemsbioinformatics.github.io/cbmpy/ and the README.md for more details. Install This version is only available from PyPI, install using: pip install cbmpy What's Changed start 0.8.5 by @bgoli in https://github.com/SystemsBioinformatics/cbmpy/pull/53 merge base updates to dev by @bgoli in https://github.com/SystemsBioinformatics/cbmpy/pull/54 Merge pull request #54 from SystemsBioinformatics/master by @bgoli in https://github.com/SystemsBioinformatics/cbmpy/pull/55 branch swap commands by @bgoli in https://github.com/SystemsBioinformatics/cbmpy/pull/56 updated actions by @bgoli in https://github.com/SystemsBioinformatics/cbmpy/pull/57 Add support for FBCv3 by @bgoli in https://github.com/SystemsBioinformatics/cbmpy/pull/58 CBMPy 0.8.7 dev merge by @bgoli in https://github.com/SystemsBioinformatics/cbmpy/pull/60 Full Changelog: https://github.com/SystemsBioinformatics/cbmpy/compare/0.8.4...0.8.8
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.387 | 0.517 |
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".