Bibliographic record
Abstract
Mandibular force profiles apply the principles of beam theory to identify mandibular biomechanical properties that reflect the bite force and feeding strategies of extant and extinct predators. While this method uses the external dimensions of the mandibular corpus to determine its biomechanical properties, more accurate results could potentially be obtained by quantifying its internal cortical bone distribution. To test this possibility, mandibular force profiles were calculated using both external mandibular dimensions ('solid mandible model') and quantification of internal bone distribution of the mandibular corpus obtained from computed tomography scans ('hollow mandible model') for five carnivorans (Canis lupus, Crocuta crocuta, Panthera leo, Neofelis nebulosa and the extinct Canis dirus). Comparison reveals that the solid model slightly overestimates mandibular biomechanical properties, but the pattern of change in biomechanical properties along the mandible remains the same. As such, feeding behavior reconstructions are consistent between the two models and are not improved by computed tomography. Bite force estimates produced by the two models are similar, except in C. crocuta, where the solid model underestimates bite force by 10-14%. This discrepancy is due to the more solid nature of the C. crocuta mandible relative to other carnivorans. Therefore, computed tomography improves bite force estimation accuracy for taxa with thicker mandibular corpora, but not significantly so otherwise. Bite force estimates derived from mandibular force profiles are far closer to empirically measured bite force than those inferred from jaw musculature dimension. Consequently, bite force estimates derived from this method can be used to calibrate finite-element analysis models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".