Bibliographic record
Abstract
0.5.6 (2023-05-08) :warning: This version will be the last version to support Python 2.x and support will be dropped in version 0.5.7. Dropped support for Python 3.5 & 3.6 and added support for Python 3.9 & 3.10. Made changes to codebase to support recent versions of numpy, Shapely and scikit-image dependencies. Added dose module with DVH class for Pythonic access to RT Dose. (#164) @cutright Added decubitus orientation and related changes. (#285) @darcymason Fix a bug if Pixel Data attribute was set for non image based SOP Classes (i.e. RT Structure Set). (#214) @cutright dvhcalc Implement interpolation for non square pixels in DVH calculation. (#124) Fix a bug where the DVHDoseScaling attribute was not applied properly to RT Dose DVHs. (#301) @cvelten Fix a bug where floating point pixel spacing wasn't rounded in DVH calculations. (#318) @smichi23 dose Added RT Dose grid summmation with interpolation (from DVHA). (#164) @cutright dicomparser Initial implementation of memory mapped access to pixel data. (#131) Ensure that all files read have a valid File Meta header. New Contributors @cutright made their first contribution in https://github.com/dicompyler/dicompyler-core/pull/164 @darcymason made their first contribution in https://github.com/dicompyler/dicompyler-core/pull/170 @smichi23 made their first contribution in https://github.com/dicompyler/dicompyler-core/pull/318 @cvelten made their first contribution in https://github.com/dicompyler/dicompyler-core/pull/301 Full Changelog: https://github.com/dicompyler/dicompyler-core/compare/v0.5.5...v0.5.6
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.500 | 0.410 |
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".