Body Mass Estimates in Dogs and North American Gray Wolves Using Limb Element Dimensions
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.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".