Comparative Morphometric Analysis of Body and Skull Parameters in German Shepherd, Golden Retriever, and Siberian Husky Dogs
Bibliographic record
Abstract
The present study involved 18 adult dogs, equally divided among three breeds: 6 German Shepherds, 6 Golden Retrievers, and 6 Siberian Huskies. The objective was to analyze various body conformation parameters, which included height, body length, heart girth, neck girth, length of the back, and overall height. Measurements were taken using a standard measuring tape to ensure accuracy. In addition to body dimensions, external skull morphometric parameters were also assessed, including skull length, skull width, cranial length, cranial width, facial length, jaw length, and key anatomical landmarks such as the prosthion, nasion, and bregma. The findings revealed that German Shepherds exhibited the highest average height compared to the other breeds, while Golden Retrievers followed closely behind, and Siberian Huskies had the lowest average height. The body length ratios among the breeds were approximately 5.50:6.50:7.0 for Siberian Husky, Golden Retriever, and German Shepherd, respectively. In terms of neck girth, the ratios were approximately 8.0:9.0:11.0 for the same breeds, with the shoulder girth ratio measured at 6.0:7.0:8.0. Moreover, the ratio of back lengths between the Golden Retriever and Siberian Husky was approximately 4.0:3.0. The cranial length ratios indicated a measurement of 4.0:3.0:3.0 for German Shepherds, Golden Retrievers, and Siberian Huskies, respectively. These results provide valuable insights into the physical characteristics of these popular dog breeds, which can aid in breed selection and understanding breed standards.
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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 | 0.000 |
| 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".