Body Mass Estimates in Dogs and North American Gray Wolves Using Limb Element Dimensions
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Bibliographic record
Abstract
Body mass is a key biometric that is useful in interpreting many aspects of an animal's life history. For many species, including dogs and wolves, methods for estimating body mass are not well developed. This paper assesses the utility of using limb dimensions to predict body mass in dogs and North American wolves. Regression analyses are utilized here to explore the correlations between limb dimensions and body masses of modern dogs and wolves, all of known body mass at death. These analyses reveal that a number of limb end dimensions are correlated with body mass in both dogs and wolves. Regression formulae generated through the analyses appear to allow body masses to be predicted with relatively small margins of error, often less than 10%. Formulae are calculated for groups with and without juveniles. In some cases, the dimensions of the juvenile specimens plot distinctly from those of adults, indicating that regression formulae specifically for juvenile canids may be needed. The strength of the limb dimension correlations is then compared with that of regression formulae for dog and wolf cranio-mandibular dimensions. For the dogs, the cranio-mandibular dimensions appear to slightly out-perform the limb element dimensions in predicting body mass. The wolf limb dimensions, however, always appear to provide better predictions of body mass than do the skull dimensions. The newly developed regression formulae are applied to several Middle Holocene dog skeletons from Siberia for which previous body mass estimates are available, the latter based on cranial dimensions. These two sets of estimates are then compared. The overall results of our study indicate the need for further research, particularly with larger sample sizes, including more juvenile specimens. We also argue that work on body size estimation in single dog breeds may be warranted in some cases. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 | 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) | 0.000 | 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 it