Exclusion of melanocortin-1 receptor (Mc1r) and Agouti as candidates for dominant black in dogs. J Hered 94:75–79
Bibliographic record
Abstract
The domestic dog exhibits a variety of coat colors that encompass a wide range of variation among different breeds. Very little is known about the molecular biology of dog pigmentation; current understanding is based mostly on traditional breeding experiments, which in some cases have suggested genetic interactions that are different from those reported in other mammals. We have examined the molecular genetics of dominant black, a uniform coat color characteristic of black Labrador retrievers or Newfoundlands that has been proposed to be caused by either variation in the melanocortin-1 receptor gene (Mc1r) or by variation in the Agouti gene (A). We identified several coding polymorphisms within Mc1r and several simple sequence repeat polymorphisms closely linked to A, and examined their inheritance in a Labrador retriever 3 greyhound cross that segregates dominant black. No single Mc1r allele was found consistently in animals carrying dominant black, and neither Mc1r nor A cosegregated with dominant black. These results refine our understanding of mammalian coat color inheritance and suggest that dominant black coat color in dogs is caused by a gene not previously implicated in pigment type switching. The study of coat color variation in mammals has resulted in the identification of many genes required for melanocyte development, migration, and regulation. A variety of pig-
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".