Photogrammetry of “Wet” Pathology Museum Specimens: A Pilot Project
Bibliographic record
Abstract
Medical museums around the world have many specimens of historical and teaching value in their collections. Some of these are bones that have been prepared to illustrate normal anatomy. Others consist of organs preserved in liquid fixative (“wet” specimens) and mounted in glass or plexiglass containers that demonstrate the pathology of disease. Digitization of these specimens has the advantage of making them available for viewing by more students or website visitors than is possible in the museum itself. Photogrammetry is one method for doing this that enables the reconstruction of high-quality 3D models using standard specimen photographs. However, although relatively easy to perform on bones, its use with “wet” objects is more difficult and special steps are required to achieve optimal results. Using specimens from the Sir William Osler aortic aneurysm collection at the Maude Abbott Medical Museum, we developed a relatively simple and cost-effective photogrammetry process that gave good reconstructions of most specimens. We expect that future developments, such as the use of artificial intelligence-based techniques, may improve this result.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".