bcgsc/pori_graphkb_python: Release v1.9.0
Bibliographic record
Abstract
What's Changed SDEV-3342 - add get_statements_from_variants function - adapted from … by @dustinbleile in https://github.com/bcgsc/pori_graphkb_python/pull/74 Feature/sdev 3256 gene list updates pharma cancer predisp by @dustinbleile in https://github.com/bcgsc/pori_graphkb_python/pull/72 Bugfix/GitHub actions timeouts by @elewis2 in https://github.com/bcgsc/pori_graphkb_python/pull/76 Bugfix/GitHub actions timeouts turn tests on by @dustinbleile in https://github.com/bcgsc/pori_graphkb_python/pull/77 Release/v1.9.0 test updates helper funcs by @dustinbleile in https://github.com/bcgsc/pori_graphkb_python/pull/78 New Contributors @elewis2 made their first contribution in https://github.com/bcgsc/pori_graphkb_python/pull/76 Full Changelog: https://github.com/bcgsc/pori_graphkb_python/compare/v1.8.0...v1.9.0 Release v1.9.0 New Features: get_statements_from_variants - helper function added. get_term_list - helper function added. get_rid - helper function added. get_pharmacogenomic_info - helper function added. get_cancer_predisposition_info - helper function added. Added constants PHARMACOGENOMIC_RELEVANCE_TERMS and PHARMACOGENOMIC_SOURCE_EXCLUDE_LIST Bugfix: Ignore gene name version in get_equivalent_features. Eg. NM_033360.4 should match 'NM_033360' and 'KRAS'. Improvements: get_preferred_gene_name - moved to genes.py move CHOMOSOMES and PREFERRED_GENE_SOURCE to constants.py add retries on connection failure added tests: test_get_pharmacogenomic_info test_get_cancer_predisposition_info reduced runtime of longest test add match test
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.287 | 0.431 |
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".