neuropoly/totalspineseg: TotalSpineSeg-r20251124
Bibliographic record
Abstract
TotalSpineSeg-r20260730 What's Changed Fix labeling issue by @NathanMolinier in https://github.com/neuropoly/totalspineseg/pull/127 Update totalspineseg version by @NathanMolinier in https://github.com/neuropoly/totalspineseg/pull/128 Consider landmark if bigger than min_component_size by @NathanMolinier in https://github.com/neuropoly/totalspineseg/pull/132 Update version by @NathanMolinier in https://github.com/neuropoly/totalspineseg/pull/133 Reduce minimum component size to 200 by @NathanMolinier in https://github.com/neuropoly/totalspineseg/pull/134 Add preprint badge and move reference to the top by @NathanMolinier in https://github.com/neuropoly/totalspineseg/pull/135 Interrupt processing if step1 fails by @NathanMolinier in https://github.com/neuropoly/totalspineseg/pull/137 Move device setup outside of inference by @NathanMolinier in https://github.com/neuropoly/totalspineseg/pull/142 Update version to 20260623 by @NathanMolinier in https://github.com/neuropoly/totalspineseg/pull/143 new nnUNetV2 weights trained using AugLab-r20260615 and elastic deformation data augmentation to make TotalSpineSeg more robust to pathologies such as scoliosis, spinal canal/cord compression. Full Changelog: https://github.com/neuropoly/totalspineseg/compare/r20251124...r20260730
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.403 | 0.451 |
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".