Bibliographic record
Abstract
What's Changed BREAKING New minimum required Rust version for compilation: 1.85.0 Features and changes Caller: feat(call): new ploidy representation by @davidlougheed in https://github.com/davidlougheed/strkit/pull/11 better locus validation various performance improvements, especially with BAM file loading and data passing new user-tuneable parameters (see documentation) JSON and log output now contains average read coverage Convert: feat(convert): revive converter with revised/more options by @davidlougheed in https://github.com/davidlougheed/strkit/pull/10 Visualization: feat(viz): add toggle kmer collapse checkbox feat(viz): add aliases for visualize command style(viz): increase visualizer font sizes Fixes Caller: Fix an issue with using the wrong normalized contig name in one place Fix a sporadic issue with non-contiguous numpy arrays Mendelian inheritance calculators: fix(mi): update straglr MI calculator columns Visualization: fix(viz): misc issues with old visualize fn Dependencies Allow newer versions of some dependencies Updates the strkit_rust_ext version Documentation Miscellaneous documentation improvements Full Changelog: https://github.com/davidlougheed/strkit/compare/v0.22.0...v0.23.0
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.505 | 0.595 |
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".