A head in virtual reality: Development of a dynamic head and neck model
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
Advances in computer and interface technologies have made it possible to create three-dimensional (3D) computerized models of anatomical structures for visualization, manipulation, and interaction in a virtual 3D environment. In the past few decades, a multitude of digital models have been developed to facilitate complex spatial learning of the human body. However, there is limited empirical evidence to guide the development and integration of effective computer models for teaching and learning. The purpose of this article is to describe the development of a dynamic head and neck model with flexible displays (2D, 3D, and stereoscopic 3D) and interactive control features that can be later used to design and test the efficacy of computer models as a means of improving student learning. The model was created using computer tomography scans of a human cadaver. Anatomical structures captured on the scans were segmented into discreet areas, and then reconstructed in three-dimensions using specialized software. The final model consists of 70 distinct anatomical structures that can be displayed in 2D, 3D, or stereoscopic 3D. In 3D mode, a mouse can be used to actively and continuously interact with the model by manipulating viewer orientation, altering surface transparency, superimposing 2D scans with 3D reconstructions, removing or adding structures sequentially, and customizing animated scenes to show complex anatomical pathways or relationships.
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 | 0.000 | 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