Bibliographic record
Abstract
What's Changed docs(readme): scalar chart by @Zeyi-Lin in https://github.com/SwanHubX/SwanLab/pull/1263 docs(readme): unified architecture by @Zeyi-Lin in https://github.com/SwanHubX/SwanLab/pull/1271 fix: csv download when self-hosted by @SAKURA-CAT in https://github.com/SwanHubX/SwanLab/pull/1275 feat: swanlab offline, online, local, disabled command by @Zeyi-Lin in https://github.com/SwanHubX/SwanLab/pull/1200 fix: change pydantic version constraint in requirements by @SAKURA-CAT in https://github.com/SwanHubX/SwanLab/pull/1276 Add verify command to check login status by @SAKURA-CAT in https://github.com/SwanHubX/SwanLab/pull/1274 feat: add interrupt handling for experiment termination by @SAKURA-CAT in https://github.com/SwanHubX/SwanLab/pull/1277 feat: deprecated pynvml by @AndPuQing in https://github.com/SwanHubX/SwanLab/pull/1282 Fix host URL construction in ExperimentAPI by @SAKURA-CAT in https://github.com/SwanHubX/SwanLab/pull/1286 Improve dependency checks and imports in metrics module by @SAKURA-CAT in https://github.com/SwanHubX/SwanLab/pull/1283 Feat/issue by @SAKURA-CAT in https://github.com/SwanHubX/SwanLab/pull/1288 New Contributors @AndPuQing made their first contribution in https://github.com/SwanHubX/SwanLab/pull/1282 Full Changelog: https://github.com/SwanHubX/SwanLab/compare/v0.6.10...v0.6.11
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.154 | 0.085 |
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".