Anatatorium: a stereoscopic three‐dimensional laboratory experience
Bibliographic record
Abstract
Student enrollment in health science and related programmes has risen dramatically over the past decade. Many core curricula require gross anatomical education but time and resource allotment for laboratory experiences are significantly reduced. The challenge for educators is to offer quality, information rich, and objective driven laboratories to provide students alternative experiences that are not repetitious of lecture presentations. The new approach described herein utilizes virtual models projected stereoscopically for laboratory groups. The virtual models are developed using CT or MR images, processed slice‐by‐slice, for tissue differentiation and identification. Exploiting dual‐source projection and passive stereo glasses, data is rendered stereoscopically offering superior depth perception and spatial orientation without viewer parallax. As models are data matrices and not two‐dimensional pictures, manipulation and close examination is possible from any orientation. Tissues may be digitally added or removed to better understand relationships in vivo. This vantage represents a significant improvement for students not normally exposed to the cadaveric wet laboratories and may offer a unique adjunct in an educational environment moving towards expanding lectures and contracting labs.
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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".