Cranial dimensions and forces of biting in the domestic dog
Bibliographic record
Abstract
The purpose of this paper is to analyse the effects of cranial size and shape in domestic dogs (Canis familiaris) on predicted forces of biting. In addition to continuous size-shape analysis, nine size-shape groups were developed based on three skull shape categories and three skull size categories. Bite forces were predicted from measurements made on dried skulls using two lever models of the skull, as well as simple models derived by regression analysis. Observed bite force values were not available for the database used in this study, so only comparisons between categories and models were undertaken. The effects of shape and size on scaled predicted bite forces were evaluated. Results show that bite force increases as size increases, and this effect was highly significant (P < 0.0001). The effect of skull shape on bite force was significant in medium and large dogs (P < 0.05). Significant differences were not evident in small dogs. Size x shape interactions were also significant (P < 0.05). Bite force predictions by the two lever models were relatively close to each other, whereas the regression models diverged slightly with some negative numbers for very small dogs. The lever models may thus be more robust across a wider range of skull size-shapes. Results obtained here would be useful to the pet food industry for food product development, as well as to paleontologists interested in methods of estimating bite force from dry skulls.
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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 | 0.003 |
| 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 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".