Bibliographic record
Abstract
Multiple neighboring organs or structures in medical images are frequently represented by labeling the underlying image (e.g. a brain into WM, GM, CSF). Given the di! erent sources of uncertainties in shape boundaries (e.g. partial volum ee ! ect and fuzzy segmentation), it is favorable to adopt a labeling approach that not only encodes uncer- tainty but also facilitates algebraic label manipulation (e.g. performing PCA). In this work, we extend the label space representation of Mal- colm et al. (1) to barycentric label space, in which a proper invertible mapping between probability vectors and label space is proposed. The probability vectors act as barycentric coe! cients describing arbitrary la- bels in label space and a non-singular matrix inversion maps points in label space back to probabilities. The elimination of conversion errors compared to the original label space mapping is demonstrated quantita- tively and qualitatively on artificial objects and brain image data, and in the context of smoothing, linear statistics, and uncertainty calculation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".