Surface modeling methods: correlation vs. unique feature extraction
Bibliographic record
Abstract
Scoliosis is a complex deformity of the trunk that induces curvature of the spine and axial rotation of individual vertebrae. Valid 3D models of the trunk surface would enable physicians to evaluate natural history and the effects of treatment for scoliosis. The authors propose a stereo vision system which works as follows. First a slide consisting of light and dark lines is projected onto the back. This slide is coded with white, black, and grey lines. The code was chosen to make correlation results easiest to use. The next step is for the operator to verify that the subject is positioned correctly. This means that the slide is covering as much of their back as possible and that the horizontal reference line is near the middle of their back. Two images are captured of the back at 640 by 480 resolution in 8 bit greyscale. The cameras that capture these images are separated by roughly 40 degrees. This is sufficient for the model accuracy that we desire. After the images are transferred to the main computer, the operator has to specify which section of the image is to be analyzed. The raw images are then analyzed by the model building system.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".