Design and validation of an open-source library of dynamic reference frames for research and education in optical tracking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dynamic reference frames (DRFs) are a common component of modern surgical tracking systems; however, the limited number of commercially available DRFs poses a constraint in developing systems, especially for research and education. This work presents the design and validation of a large, open-source library of DRFs compatible with passive, single-face tracking systems, such as Polaris stereoscopic infrared trackers (NDI, Waterloo, Ontario). An algorithm was developed to create new DRF designs consistent with intra- and intertool design constraints and convert to computer-aided design (CAD) files suitable for three-dimensional printing. A library of 10 such groups, each with 6 to 10 DRFs, was produced and tracking performance was validated in comparison to a standard commercially available reference, including pivot calibration, fiducial registration error (FRE), and target registration error (TRE). Pivot tests showed calibration error [Formula: see text], indistinguishable from the reference. FRE was [Formula: see text], and TRE in a CT head phantom was [Formula: see text], both equivalent to the reference. The library of DRFs offers a useful resource for surgical navigation research and could be extended to other tracking systems and alternative design constraints.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it