Sexual dimorphism of Labrador Retriever dogs by morphometry
Bibliographic record
Abstract
The domestic dog (Canis familiaris) is the species of greatest morphological diversity among mammals. Seventy-four Labrador Retriever dogs- 27 males and 47 females - were used in this experiment. Thirty quantitative biometric characteristics, related to morphology were measured. The objective of this study was to evaluate the morphometric traits of the Labrador Retriever breed to establish descriptive biometric attributes that may show sexual dimorphism through principal component analysis (PCA) and discriminant analysis (DA). The PCA was processed using all the variables and performing a pre-selection of the most correlated variables. The DA was performed for the 30 variables and also for the five most correlated variables with the first component (CP1), in order to classify new individuals. The PCA was able to identify sexual dimorphism in size, with both the 30 original variables as with the pre- selected variables, the latter optimized the reduction to two principal components. The DA was able to discriminate the two populations, both for 30 variables as for the five variables most correlated with the CP1. The functions with five variables can be used to classify other purebred dogs for sex, with an error of about 6.75%.
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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 | 0.001 |
| 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".