Bibliographic record
Abstract
Changes: 74ae0fe8f14fc63b64997be0070067654ff3094a [MRG] Using Mamba in Pipelines (#1210) 56f6acd694035e412316b21f8d31e0c543d6c245 Add new Contribution guidelines (#1237) e2ea846ef69180c7d6ae6b3fac8e5f1d8073faaf code of conduct symlink (#1238) 210f89ddbe7bb1d9bda20b2e8c3f2ee4ad337a25 TARDIS Code of Conduct (#1234) 2c682864b95616232b1f668545a9f92b89dc67ba Update of the Governance model (#1233) 6095cea1631daca33cae578cb8311b9cbf92001f Roadmap Documentation Page (#1231) cd35df9ba8106efcf8244bad711b1f95187369ae Update base.py (#1227) This list of changes was auto generated.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.682 | 0.766 |
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".