Stereoscopic three‐dimensional reconstruction of the female pelvis and pelvic contents for education
Bibliographic record
Abstract
The anatomy of the pelvis is complex, and its three‐dimensional organization is conceptually difficult for students to grasp. The aim of this project is to create an explorable and projectable stereoscopic three‐dimensional (3‐D) model of the female pelvis and pelvic contents for anatomical education. The model is created using cyrosection images obtained from the Visible Human Project in conjunction with a general purpose three‐dimensional segmentation and surface rendering program. Anatomical areas of interest are identified and labeled on consecutive images. They are then reassembled into a three‐dimensional model. Currently the model includes the pelvic girdle, organs of the pelvic cavity, surrounding muscles, the perineum, and large blood vessels. Each structure can be individually controlled (ex. added, subtracted, made transparent) in order to reveal the organization of and relationships between structures. The model can also be manipulated and/or projected stereoscopically in order to visualize structures and relationships from different angles with excellent spatial perception. Due to its ease of use and versatility this model may provide powerful teaching tool for learning in the classroom or in the laboratory. Research support: Ontario Graduate Scholarship. Grant Funding Source Internal
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".