Bibliographic record
Abstract
Cream dogs of several breeds require a genotype of e/e at MC1R based on 27 individuals in this study. All Akita, Caucasian Mountain Dogs, German Shepherd Dogs, Miniature Schnauzer, and Puli with this genotype are cream, suggesting they are fixed at a second locus which causes the phaeomelanin pigmentation caused by this genotype to be diluted or pale. Conversely, although all Chinese Shar-Pei and Poodles that were cream had an e/e genotype at MC1R, not all dogs with this genotype are cream. Today, many Golden Retrievers and Labrador Retrievers with an e/e genotype are cream instead of the traditional yellow to golden color seen in the past. The second gene in these breeds must have multiple alleles, only one of which causes phaeomelanin pigment to be diluted or pale. Tyrosinase (TYR) and solute carrier family 45, member 2 (SLC45A2) have been shown to cause cream coat color in other species and were therefore investigated in dogs as candidate genes for this second locus. Although polymorphisms were detected in cDNA sequence from TYR and SLC45A2, no polymorphism was consistently associated with cream dogs or cosegregated with cream coat color in any of the families used in this study. A microsatellite was detected in a published BAC sequence (GenBank no. AAEX01017083) in intron 2 and was used to map SLC45A2 to CFA4.
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 | 0.000 | 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) | 0.002 | 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".