Canine Skull Digitalization and Three-Dimensional Printing as an Educational Tool for Anatomical Study
Bibliographic record
Abstract
This article aims to standardize 3D scanning and printing of dog skulls for educational use and evaluate the effectiveness of these anatomical printed models for a veterinary anatomy course. Skulls were selected for scanning and creating 3D-printed models through Fused Deposition Modeling using acrylonitrile-butadiene-styrene. After a lecture on skull anatomy, the 3D-printed and real skull models were introduced during the practical bone class to 140 students. A bone anatomy practical test was conducted after a month; it consisted in identifying previously marked anatomical structures of the skull bones. The students were divided into two groups for the exam; the first group of students took the test on the real skulls, whereas the second group of students took the test on 3D-printed skulls. The students' performance was evaluated using similar practical examination questions. At the end of the course, these students were asked to answer a brief questionnaire about their individual experiences. The results showed that the anatomical structures of the 3D-printed skulls were similar to the real skulls. There was no significant difference between the test scores of the students that did their test using the real skulls and those using 3D prints. In conclusion, it was possible to construct a dynamic and printed digital 3D collection for studies of the comparative anatomy of canine skull species from real skulls, suggesting that 3D-digitalized and-printed skulls can be used as tools in veterinary anatomy teaching.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".