Bibliographic record
Abstract
What's Changed feat: env add gpu nvidia architecture and cudacores by @Zeyi-Lin in https://github.com/SwanHubX/SwanLab/pull/901 feat: Add Object3D spatiallm example by @xj63 in https://github.com/SwanHubX/SwanLab/pull/905 feat: ascend cann version by @Zeyi-Lin in https://github.com/SwanHubX/SwanLab/pull/906 feat: swanlab.Settings -- A Global Feature Switch Management Module by @happy-riosky in https://github.com/SwanHubX/SwanLab/pull/903 Fix: Change Dict to dict in _TYPE_HANDLERS type hints by @xj63 in https://github.com/SwanHubX/SwanLab/pull/904 feat(plugin): support extra discord & slack callback by @Nexisato in https://github.com/SwanHubX/SwanLab/pull/910 fix(docs): en redirection by @Nexisato in https://github.com/SwanHubX/SwanLab/pull/911 feat: gpu time of memory by @kites262 in https://github.com/SwanHubX/SwanLab/pull/912 refactor: settings by @SAKURA-CAT in https://github.com/SwanHubX/SwanLab/pull/914 feat: env cambricon by @Zeyi-Lin in https://github.com/SwanHubX/SwanLab/pull/915 fix snyc wandb finish by @Zeyi-Lin in https://github.com/SwanHubX/SwanLab/pull/918 feat: new env by @SAKURA-CAT in https://github.com/SwanHubX/SwanLab/pull/921 docs: readme 0.5.4 by @Zeyi-Lin in https://github.com/SwanHubX/SwanLab/pull/919 New Contributors @happy-riosky made their first contribution in https://github.com/SwanHubX/SwanLab/pull/903 Full Changelog: https://github.com/SwanHubX/SwanLab/compare/v0.5.3...v0.5.4
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.498 | 0.549 |
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".