3D scanning and printing skeletal tissues for anatomy education
Bibliographic record
Abstract
Detailed anatomical models can be produced with consumer-level 3D scanning and printing systems. 3D replication techniques are significant advances for anatomical education as they allow practitioners to more easily introduce diverse or numerous specimens into classrooms. Here we present a methodology for producing anatomical models in-house, with the chondrocranium cartilage from a spiny dogfish (Squalus acanthias) and the skeleton of a cane toad (Rhinella marina) as case studies. 3D digital replicas were produced using two consumer-level scanners and specimens were 3D-printed with selective laser sintering. The fidelity of the two case study models was determined with respect to key anatomical features. Larger-scale features of the dogfish chondrocranium and frog skeleton were all well-resolved and distinct in the 3D digital models, and many finer-scale features were also well-resolved, but some more subtle features were absent from the digital models (e.g. endolymphatic foramina in chondrocranium). All characters identified in the digital chondrocranium could be identified in the subsequent 3D print; however, three characters in the 3D-printed frog skeleton could not be clearly delimited (palatines, parasphenoid and pubis). Characters that were absent in the digital models or 3D prints had low-relief in the original scanned specimen and represent a minor loss of fidelity. Our method description and case studies show that minimal equipment and training is needed to produce durable skeletal specimens. These technologies support the tailored production of models for specific classes or research aims.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".