Is solid always best? Cranial performance in solid and fenestrated caecilian skulls
Bibliographic record
Abstract
Caecilians (Lissamphibia: Gymnophiona) are characterized by a fossorial lifestyle that appears to play a role in the many anatomical specializations in the group. The skull, in particular, has been the focus of previous studies because it is driven into the substrate for burrowing. There are two different types of skulls in caecilians: (1) stegokrotaphic, where the squamosal completely covers the temporal region and the jaw closing muscles, and (2) zygokrotaphic, with incomplete coverage of the temporal region by the squamosal. We used 3-D imaging and modeling techniques to explore the functional consequences of these skull types in an evolutionary context. We digitally converted stegokrotaphic skulls into zygokrotaphic skulls and vice versa. We also generated a third, akinetic skull type that was presumably present in extinct caecilian ancestors. We explored the benefits and costs of the different skull types under frontal loading at different head angles with finite element analysis (FEA). Surprisingly, the differences in stress distributions and bending between the three tested skull types were minimal and not significant. This suggests that the open temporal region in zygokrotaphic skulls does not lead to poorer performance during burrowing. However, the results of the FEA suggest a strong relationship between the head angle and skull performance, implying there is an optimal head angle during burrowing.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".