Inheritance of different coat colours in Newfoundland dogs in Poland
Bibliographic record
Abstract
The aim of the study was to present the manner in which coat colour genes are inherited in the Newfoundland dog breed and to estimate the number of dogs with various coat colours in the Polish Newfoundland dog population in 2017. This population numbered 656 dogs, including 248 males and 408 females. The estimated number of dogs of this breed also included all registered puppies, broken down by gender and coat colour. The genes determining coat colour are described, including more precisely the genes responsible for the coat colour of the Newfoundland breed. According to FCI regulations, the coat colours for Newfoundland dogs are black, brown and black-and-white. Other colours, such as brown-and-white or blue, are not recognized for breeding purposes in Europe. The study found that the dominant black coat was predominant in the Polish Newfoundland dog population in 2017. These dogs could be heterozygous at some other loci and have undesirable alleles. The second most common coat colour was chocolate, while the fewest dogs had spotted coats. The group with spotted coats contained more males than females, in contrast to the other two colour variants. There were also individuals with the blue coat colour, which is not accepted for breeding, as the result of mating of parents with proper coat colours. An understanding of how dog coat colours are inherited and the need for tests to determine coat colour genotypes would make it possible to foresee the occurrence of incorrect colours in subsequent generations, which is crucial for Newfoundland dog breeders, whose goal is to obtain dogs whose coat colour is in line with the FCI standard.
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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 | 0.001 |
| 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".