Testing Adaptive Hypotheses of Convergence with Functional Landscapes: A Case Study of Bone-Cracking Hypercarnivores
Why this work is in the frame
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Bibliographic record
Abstract
Morphological convergence is a well documented phenomenon in mammals, and adaptive explanations are commonly employed to infer similar functions for convergent characteristics. I present a study that adopts aspects of theoretical morphology and engineering optimization to test hypotheses about adaptive convergent evolution. Bone-cracking ecomorphologies in Carnivora were used as a case study. Previous research has shown that skull deepening and widening are major evolutionary patterns in convergent bone-cracking canids and hyaenids. A simple two-dimensional design space, with skull width-to-length and depth-to-length ratios as variables, was used to examine optimized shapes for two functional properties: mechanical advantage (MA) and strain energy (SE). Functionality of theoretical skull shapes was studied using finite element analysis (FEA) and visualized as functional landscapes. The distribution of actual skull shapes in the landscape showed a convergent trend of plesiomorphically low-MA and moderate-SE skulls evolving towards higher-MA and moderate-SE skulls; this is corroborated by FEA of 13 actual specimens. Nevertheless, regions exist in the landscape where high-MA and lower-SE shapes are not represented by existing species; their vacancy is observed even at higher taxonomic levels. Results highlight the interaction of biomechanical and non-biomechanical factors in constraining general skull dimensions to localized functional optima through evolution.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it