Model‐Based Identification of Mechanical Characteristics of <i>Sinosaurus</i> (Theropoda) Crests
Bibliographic record
Abstract
Abstract The paired cranial crests of Sinosaurus (Theropoda) have been hypothesized as too weak to resist mechanical loads during combat. Finite element analysis (FEA) is used to test this hypothesis, first with geometry obtained through direct laser scanning of a well‐preserved fossil of the crest, and then with two conceptual FE models of both crests analyzing the structure‐deformation effects of fenestration. In the original fossil model, under direct loading on the dorsal faces of the crest, we found that the areas surrounding cavities on the crest experience shear stress that implies a high chance of material failure – the fracture of bone. In the conceptual model, a series of computational studies were conducted with varying loading directions. One simulation found that the shear stress and strain in the material around the cavity presented more deformation compared with the conceptual model without the cavities, and under this morphologically realistic scenario the loading conditions would result in local bone fractures. These model‐based computational results indicate that the crest could not resist high loads, because it could not effectively decentralize the loading stress. Future investigations need to focus on more comprehensive computational experiments with more conditions, e.g. dynamical loading conditions, and direct palaeontological evidence.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".