Mass Properties of the Pig Mandible
Bibliographic record
Abstract
Specification of mass properties is an essential step in the modeling of jaw dynamics, but obtaining them can be difficult. Here, we used three-dimensional computed tomography (CT) to estimate jaw mass, mean bone density, anatomical locations of the mass and geometric centers, and moments of inertia in the pig jaw. High-resolution CT scans were performed at one-mm slice intervals on specimens submerged in water. The mean estimated jaw mass was 12% greater than the mean wet weight, and 33% more than the mean dry weight. Putative bone marrow accounted for an extra 13% of mass. There was a positive correlation between estimated mean bone density and age. The mass center was consistently in the midline, near the last molar. The mean distance between the mass center and geometric center was small, especially when bone marrow was taken into account (0.58 +/- 0.21 mm), suggesting that mass distribution in the pig jaw is almost symmetrical with respect to its geometric center. The largest moment of inertia occurred around each mandible's supero-inferior axis, and the smallest around its antero-posterior axis. Bone marrow contributed an extra 9% to the moments of inertia in all three axes. Linear relationships were found between the actual mass and a mass descriptor (product of the bounding volume and mean bone density), and between the moments of inertia and moments of inertia descriptors (products of the mass descriptor and two orthogonal dimensions forming the bounding box). The study suggests that imaging modalities revealing three-dimensional jaw shape may be adequate for estimating the bone mass properties in pigs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".