Bibliographic record
Abstract
What's Changed [issue_tracker] Do not display inactive users in issue form by @charlottesce in https://github.com/aces/Loris/pull/8841 [LINST] skip metadatafields for surveys by @zaliqarosli in https://github.com/aces/Loris/pull/8961 [tools] Fix efficiency of fix_candidate_age script by @CamilleBeau in https://github.com/aces/Loris/pull/8992 [media] fixes issue when media module try to instantiate a non existing instrument. by @racostas in https://github.com/aces/Loris/pull/8903 [LINST] fix survey and date issues by @ridz1208 in https://github.com/aces/Loris/pull/8858 [JSX] Modal Width Prop by @skarya22 in https://github.com/aces/Loris/pull/8893 [libraries] Get examiner sites by UserID by @CamilleBeau in https://github.com/aces/Loris/pull/8994 [genomic_browser] Download button not showing up in the Files tab by @ridz1208 in https://github.com/aces/Loris/pull/8480 [issue_tracker - raisinbread/tools] Fix issue tracker history wrong module ID by @zaliqarosli in https://github.com/aces/Loris/pull/8677 [Issue Tracker] Issue Change Notifications by @skarya22 in https://github.com/aces/Loris/pull/8885 [behavioural_qc] fixes visitLevel feedback not showing up. by @racostas in https://github.com/aces/Loris/pull/8900 Full Changelog: https://github.com/aces/Loris/compare/v24.1.5...v24.1.6
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.204 | 0.169 |
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; both teacher heads agree on what is shown here.
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".