Calibration of estimated biting forces in domestic canids: comparison of post‐mortem and <i>in vivo</i> measurements
Bibliographic record
Abstract
Estimates of biting forces are widely used in paleontological and comparative studies of feeding mechanics and performance, and are usually derived from lever models based on measurements made on the skull that are relevant to the mechanics of the masticatory system. Owing to assumptions and unmeasurable errors in their estimation, such values are used comparatively rather than as absolute estimates. The purpose of this paper was to provide calibration of post-mortem calculated bite force estimates by comparing them to in vivo forces derived from a sample of 20 domestic dogs (Canis familiaris) during muscle stimulation under general anaesthesia. Two lever models previously described in the literature were used to estimate post-mortem values, and regression analysis was also performed to derive best-fit equations against a number of morphometric measurements on the skull. The ranges of observed forces in vivo were 147-946 N at the canine, and 524-3417 N at the second molar. The lever models substantially underestimated these forces, giving mean values between 39% and 61% of the observed means. Predictability was considerably improved by removing the linear bias and deviation of the regression slope from unity with an adjustment equation. Best-fit statistical models developed on these animals performed considerably better (calculated means within 0.54% of observed means) and included easily measureable variables such as bodyweight, dimensions of the temporalis fossa and out-lever from the jaw joint to the biting tooth. These data should lead to more accurate absolute, rather than relative, estimates of biting forces for other extant and fossil canids, and other carnivorans by extrapolation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".