Bibliographic record
Abstract
What's Changed This is a bugfix release. It fixes the following problem(s): Fix a couple of issues with the new --top-score-metric2 by @susannasiebert in https://github.com/griffithlab/pVACtools/pull/1291 When adding the --top-score-metric2 option, the logic for determining the included candidates during aggregate report creation was amended to compare either the IC50 or percentile to the aggregate inclusion binding threshold. This logic should not have been changed and instead only the IC50 should be compared to the aggregate inclusion binding threshold, no matter which --top-score-metric2 was selected. This specific change has been reverted In order to achieve deterministic results when using the percentile --top-score-metric2 option, a peptides.sort() call was used. This release replaces this with a better way of finding the best peptide by using the IC50 as a secondary sort criteria. Full Changelog: https://github.com/griffithlab/pVACtools/compare/v5.5.0...v5.5.1
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.367 | 0.425 |
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".