Deep learning-aided segmentation combined with finite element analysis reveals a more natural biomechanic of dinosaur fossil
Bibliographic record
Abstract
Finite element analysis (FEA), a biomechanical simulation technique capable of providing direct mechanical visualization for CT-based digital models, has been extensively applied to fossil image datasets to address key evolutionary questions in paleontology. However, the rock matrix filling intertrabecular space of fossils often causes severe deviations in FEA results. Segmentation strategies such as thresholding and manual labeling have been employed to mitigate these disturbances. However, the efficiency of manual segmentation and the accuracy of thresholding remain questionable. In this study, we applied FEA combined with deep learning-based segregation on a femoral specimen of Jeholosaurus (a small bipedal dinosaur). This novel methodology efficiently generates the FE model with stress distribution that closely reflects the trabecular architecture in fossils of extinct taxa, reflecting a more natural state of biomechanical performance with high biological reality. Our approach provides a practical strategy for studying the biomechanics, functional morphology, and taxonomy of extinct species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".