Canine coloration: dark domesticated dogs and the bright side for wild wolves
Bibliographic record
Abstract
The skin, feathers, and fur of most wild animals are pigmented with melanin, and these black, brown, gray, rufous, and buff colors serve many evolutionary functions. Melanic patterns – alone or in combination with other traits – can act as bold signals of fighting ability, can amplify other signaling characteristics, or, as is most commonly recognized, can create a camouflaged appearance. In many species where natural selection favors cryptic coloration, melanin pigment and pattern variation across individuals is notably low; however, in other instances (most memorably including the peppered moth), remarkable variation remains, and these present exciting opportunities to study both the causes and consequences of melanization in nature. Melanin coloration, with little interindividual variation within species, is generally the rule among mammals, with noteworthy exceptions among domesticated varieties and a few wild species. Gray wolves (Canis lupus), for example, vary widely among and within western North American habitats in their coat color (e.g. white, gray, and black). Technological advances in recent decades have made it possible to study the molecular genetic bases to such melanin variability. From zebra fish to chickens to rodents, numerous loci have been uncovered that control the melanin synthesis pathway and pigment variations in domesticated species and humans, with special attention paid to the melanocortin-1 receptor (MC1R;
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".