Dog sperm swimming parameters analysed by computer‐assisted semen analysis of motility reveal major breed differences
Bibliographic record
Abstract
Contents Dogs have undergone an intensive artificial selection process ever since the beginning of their relationship with humans. As a consequence, a wide variety of well‐defined breeds exist today. Due to the enormous variation in dog phenotypes and the unlikely chance of gene exchange between them, the question arises as to whether they should still be regarded as a single species or, perhaps, they be considered as different taxa that possess different reproductive traits. The aim of this study was therefore to characterize some male reproductive traits, focusing on kinematic characteristics of dog spermatozoa from several breeds. Thirty‐seven dogs from the following breeds were used: Staffordshire Bull Terrier, Labrador Retriever, Spanish Mastiff, Valencian Rat Hunting Dog, British Bulldog and Chihuahua. Semen samples were obtained via manual stimulation and diluted to a final sperm concentration of 50 million/ml, and they were subsequently analysed by the computer assisted semen analysis (CASA‐Mot) ISAS®v1 system. Eight kinematic parameters were evaluated automatically. All parameters showed significant different values among breeds and among individuals within each breed. The fastest sperm cells were those of Staffordshire Bull Terriers and the slowest were recorded in Chihuahuas. The intra‐male coefficient of variation (CV) was higher than the inter‐male CV for all breeds with the Staffordshire Bull Terrier showing the lowest values. When taking into consideration the cells by animal and breed, discriminant analyses showed a high capability to predict the breed. Cluster analyses showed a hierarchical classification very close to that obtained after phylogenetic studies with genome markers. In conclusion, future workers on dog spermatozoa should bear in mind major differences between breeds and realize that results cannot be extrapolated from one to another. Because sperm characteristics are associated with breed diversity, dogs may represent a good model to examine changes in reproductive parameters associated with selection processes.
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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 | 0.000 |
| 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) | 0.001 | 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".