spine-generic/data-multi-subject: r20230223
Bibliographic record
Abstract
What's Changed Add git annex init to download instructions by @mguaypaq in https://github.com/spine-generic/data-multi-subject/pull/114 Fix defaced images by @alexfoias in https://github.com/spine-generic/data-multi-subject/pull/119 add brain_t1 category into exclude.yml file (Issue #126) by @mbondy023 in https://github.com/spine-generic/data-multi-subject/pull/127 Add instruction for working from a fork by @mguaypaq in https://github.com/spine-generic/data-multi-subject/pull/129 Move derivatives from spine-generic-processed by @sandrinebedard in https://github.com/spine-generic/data-multi-subject/pull/123 Update README.md by @jcohenadad in https://github.com/spine-generic/data-multi-subject/pull/130 Modify derivatives softsegs in sb/add_extra_manual_seg by @mguaypaq in https://github.com/spine-generic/data-multi-subject/pull/133 Add labels 1 and 2 to T2w_labels-disc-manual by @valosekj in https://github.com/spine-generic/data-multi-subject/pull/134 Remove extra subjects from derivatives/labels by @mguaypaq in https://github.com/spine-generic/data-multi-subject/pull/138 Fix file permissions by @mguaypaq in https://github.com/spine-generic/data-multi-subject/pull/139 Rename MTS suffix by @sandrinebedard in https://github.com/spine-generic/data-multi-subject/pull/135 Add pathology and notes entries by @valosekj in https://github.com/spine-generic/data-multi-subject/pull/140 New Contributors @mbondy023 made their first contribution in https://github.com/spine-generic/data-multi-subject/pull/127 @sandrinebedard made their first contribution in https://github.com/spine-generic/data-multi-subject/pull/123 Full Changelog: https://github.com/spine-generic/data-multi-subject/compare/r20220125...r20230223
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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.888 | 0.914 |
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; the direct Gemma label and the distilled Codex classifier 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".