Separating Wild from Domestic American Mink <i>Neovison vison</i> Based on Skull Morphometries
Bibliographic record
Abstract
Domestication can change animal traits such as skull size and shape. Given that domestic American mink Neovison vison may escape from farms within the native range of wild mink, we were interested in determining whether 1) skull characteristics differed between wild and domestic mink, and 2) free ranging mink in Ontario had skull features characteristic of domestic animals. Contrary to previous research, we found no effect of domestication on braincase volume or muzzle length in mink. We did, however, find that skulls of domestic mink were larger than those of wild mink and that domestic skulls had narrowed postorbital constrictions (POC). A model using both condylobasal length (CBL) and POC correctly classified the origin of 100% of male skulls and 90% of female skulls in an external data set. A POC‐only model was less successful, however, correctly classifying 68% and 70% of male and female skulls, respectively. In a field application of the two‐term model, only one of 109 skulls was identifed as being of farm origin. With the POC‐only model, however, 12 skulls were classified as being from domestic animals. Where size differences are expected (for example, with recently escaped animals), the model should be effective for identifying domestic mink. However, the utility of CBL and POC for identifying domestic‐origin or hybrid animals that have been born in the wild depends on a key uncertainty, namely, the extent to which these traits have a genetic basis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 | 0.001 |
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 teacher head, 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".